diff --git a/docs/notebooks/single_cell/02_2_1_scatac_multiome_pancreas_priors_train.ipynb b/docs/notebooks/single_cell/02_2_1_scatac_multiome_pancreas_priors_train.ipynb
index 5c327b5..8b49730 100644
--- a/docs/notebooks/single_cell/02_2_1_scatac_multiome_pancreas_priors_train.ipynb
+++ b/docs/notebooks/single_cell/02_2_1_scatac_multiome_pancreas_priors_train.ipynb
@@ -5,7 +5,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Mouse pancreatic endocrinogenesis (scATAC-seq) | Training with a RNA-dynamics kNN-graph"
+    "## Mouse pancreatic endocrinogenesis (scATAC-seq) | Training with an RNA-dynamics kNN-graph"
    ]
   },
   {
@@ -19,23 +19,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2"
@@ -43,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -67,6 +58,16 @@
      "text": [
       "/home/ilibarra/workspace/theislab/mubind/docs/notebooks/single_cell\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ilibarra/miniconda3/envs/mubind/lib/python3.9/site-packages/IPython/core/magics/osm.py:393: UserWarning: using bookmarks requires you to install the `pickleshare` library.\n",
+      "  bkms = self.shell.db.get('bookmarks', {})\n",
+      "/home/ilibarra/miniconda3/envs/mubind/lib/python3.9/site-packages/IPython/core/magics/osm.py:417: UserWarning: using dhist requires you to install the `pickleshare` library.\n",
+      "  self.shell.db['dhist'] = compress_dhist(dhist)[-100:]\n"
+     ]
     }
    ],
    "source": [
@@ -75,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -96,7 +97,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -117,7 +118,7 @@
        "286"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -131,7 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -146,7 +147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -166,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -207,7 +208,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -221,7 +222,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rm data/scatac/pancreas_multiome/pancreas_multiome_2022_processed_atac.h5ad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -233,19 +243,45 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "True data/scatac/pancreas_multiome/pancreas_multiome_2022_processed_rna_velocities_2024.h5ad\n",
-      "True data/scatac/pancreas_multiome/pancreas_multiome_2022_processed_atac.h5ad\n"
+      "True data/scatac/pancreas_multiome/pancreas_multiome_2022_processed_rna_velocities_2024.h5ad\n"
      ]
     },
     {
-     "data": {
-      "text/plain": [
-       "((16918, 14663), (16918, 228259))"
-      ]
-     },
-     "execution_count": 83,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "opening RNA successful\n",
+      "False data/scatac/pancreas_multiome/pancreas_multiome_2022_processed_atac.h5ad\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 175k/175k [00:00<00:00, 1.07MB/s]\n"
+     ]
+    },
+    {
+     "ename": "OSError",
+     "evalue": "Unable to synchronously open file (file signature not found)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[101], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# load the pancreas multiome dataset\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m rna, atac \u001b[38;5;241m=\u001b[39m \u001b[43mmb\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdatasets\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpancreas_multiome\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# data_directory='../../../annotations/scatac')\u001b[39;00m\n\u001b[1;32m      3\u001b[0m rna\u001b[38;5;241m.\u001b[39mshape, atac\u001b[38;5;241m.\u001b[39mshape\n",
+      "File \u001b[0;32m~/workspace/theislab/mubind/mubind/datasets/datasets.py:563\u001b[0m, in \u001b[0;36mpancreas_multiome\u001b[0;34m(data_directory)\u001b[0m\n\u001b[1;32m    560\u001b[0m rna_path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(data_directory, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpancreas_multiome_2022_processed_rna_velocities_2024.h5ad\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    562\u001b[0m rna \u001b[38;5;241m=\u001b[39m pancreas_rna()\n\u001b[0;32m--> 563\u001b[0m atac \u001b[38;5;241m=\u001b[39m \u001b[43mpancreas_atac\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    566\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m rna, atac\n",
+      "File \u001b[0;32m~/workspace/theislab/mubind/mubind/datasets/datasets.py:549\u001b[0m, in \u001b[0;36mpancreas_atac\u001b[0;34m(file_path)\u001b[0m\n\u001b[1;32m    547\u001b[0m url \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhttps://www.dropbox.com/scl/fi/53wv4v7tbnsmr12fbmea7/pancreas_multiome_2022_processed_atac.h5ad?rlkey=1kf352wya0pzffkn990wkbwmd&e=1&st=m6gv9hp5&dl=1\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    548\u001b[0m \u001b[38;5;28mprint\u001b[39m(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(file_path), file_path)\n\u001b[0;32m--> 549\u001b[0m adata \u001b[38;5;241m=\u001b[39m \u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbackup_url\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m    550\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mopening ATAC successful\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    551\u001b[0m adata\u001b[38;5;241m.\u001b[39mvar_names_make_unique()\n",
+      "File \u001b[0;32m~/miniconda3/envs/mubind/lib/python3.9/site-packages/legacy_api_wrap/__init__.py:80\u001b[0m, in \u001b[0;36mlegacy_api.<locals>.wrapper.<locals>.fn_compatible\u001b[0;34m(*args_all, **kw)\u001b[0m\n\u001b[1;32m     77\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(fn)\n\u001b[1;32m     78\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfn_compatible\u001b[39m(\u001b[38;5;241m*\u001b[39margs_all: P\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw: P\u001b[38;5;241m.\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m R:\n\u001b[1;32m     79\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args_all) \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m n_positional:\n\u001b[0;32m---> 80\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs_all\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     82\u001b[0m     args_pos: P\u001b[38;5;241m.\u001b[39margs\n\u001b[1;32m     83\u001b[0m     args_pos, args_rest \u001b[38;5;241m=\u001b[39m args_all[:n_positional], args_all[n_positional:]\n",
+      "File \u001b[0;32m~/miniconda3/envs/mubind/lib/python3.9/site-packages/scanpy/readwrite.py:124\u001b[0m, in \u001b[0;36mread\u001b[0;34m(filename, backed, sheet, ext, delimiter, first_column_names, backup_url, cache, cache_compression, **kwargs)\u001b[0m\n\u001b[1;32m    122\u001b[0m filename \u001b[38;5;241m=\u001b[39m Path(filename)  \u001b[38;5;66;03m# allow passing strings\u001b[39;00m\n\u001b[1;32m    123\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_valid_filename(filename):\n\u001b[0;32m--> 124\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    125\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    126\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbacked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbacked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    127\u001b[0m \u001b[43m        \u001b[49m\u001b[43msheet\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msheet\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    128\u001b[0m \u001b[43m        \u001b[49m\u001b[43mext\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mext\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    129\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdelimiter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdelimiter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    130\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfirst_column_names\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfirst_column_names\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    131\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbackup_url\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbackup_url\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    132\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    133\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcache_compression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_compression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    134\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    135\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    136\u001b[0m \u001b[38;5;66;03m# generate filename and read to dict\u001b[39;00m\n\u001b[1;32m    137\u001b[0m filekey \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(filename)\n",
+      "File \u001b[0;32m~/miniconda3/envs/mubind/lib/python3.9/site-packages/scanpy/readwrite.py:759\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filename, backed, sheet, ext, delimiter, first_column_names, backup_url, cache, cache_compression, suppress_cache_warning, **kwargs)\u001b[0m\n\u001b[1;32m    757\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ext \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mh5\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mh5ad\u001b[39m\u001b[38;5;124m\"\u001b[39m}:\n\u001b[1;32m    758\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m sheet \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 759\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mread_h5ad\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbacked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbacked\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    760\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    761\u001b[0m         logg\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mreading sheet \u001b[39m\u001b[38;5;132;01m{\u001b[39;00msheet\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m from file \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfilename\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[0;32m~/miniconda3/envs/mubind/lib/python3.9/site-packages/anndata/_io/h5ad.py:237\u001b[0m, in \u001b[0;36mread_h5ad\u001b[0;34m(filename, backed, as_sparse, as_sparse_fmt, chunk_size)\u001b[0m\n\u001b[1;32m    229\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[1;32m    230\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCurrently only `X` and `raw/X` can be read as sparse.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    231\u001b[0m         )\n\u001b[1;32m    233\u001b[0m rdasp \u001b[38;5;241m=\u001b[39m partial(\n\u001b[1;32m    234\u001b[0m     read_dense_as_sparse, sparse_format\u001b[38;5;241m=\u001b[39mas_sparse_fmt, axis_chunk\u001b[38;5;241m=\u001b[39mchunk_size\n\u001b[1;32m    235\u001b[0m )\n\u001b[0;32m--> 237\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mh5py\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mFile\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m    239\u001b[0m     \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcallback\u001b[39m(func, elem_name: \u001b[38;5;28mstr\u001b[39m, elem, iospec):\n\u001b[1;32m    240\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m iospec\u001b[38;5;241m.\u001b[39mencoding_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124manndata\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m elem_name\u001b[38;5;241m.\u001b[39mendswith(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n",
+      "File \u001b[0;32m~/miniconda3/envs/mubind/lib/python3.9/site-packages/h5py/_hl/files.py:562\u001b[0m, in \u001b[0;36mFile.__init__\u001b[0;34m(self, name, mode, driver, libver, userblock_size, swmr, rdcc_nslots, rdcc_nbytes, rdcc_w0, track_order, fs_strategy, fs_persist, fs_threshold, fs_page_size, page_buf_size, min_meta_keep, min_raw_keep, locking, alignment_threshold, alignment_interval, meta_block_size, **kwds)\u001b[0m\n\u001b[1;32m    553\u001b[0m     fapl \u001b[38;5;241m=\u001b[39m make_fapl(driver, libver, rdcc_nslots, rdcc_nbytes, rdcc_w0,\n\u001b[1;32m    554\u001b[0m                      locking, page_buf_size, min_meta_keep, min_raw_keep,\n\u001b[1;32m    555\u001b[0m                      alignment_threshold\u001b[38;5;241m=\u001b[39malignment_threshold,\n\u001b[1;32m    556\u001b[0m                      alignment_interval\u001b[38;5;241m=\u001b[39malignment_interval,\n\u001b[1;32m    557\u001b[0m                      meta_block_size\u001b[38;5;241m=\u001b[39mmeta_block_size,\n\u001b[1;32m    558\u001b[0m                      \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[1;32m    559\u001b[0m     fcpl \u001b[38;5;241m=\u001b[39m make_fcpl(track_order\u001b[38;5;241m=\u001b[39mtrack_order, fs_strategy\u001b[38;5;241m=\u001b[39mfs_strategy,\n\u001b[1;32m    560\u001b[0m                      fs_persist\u001b[38;5;241m=\u001b[39mfs_persist, fs_threshold\u001b[38;5;241m=\u001b[39mfs_threshold,\n\u001b[1;32m    561\u001b[0m                      fs_page_size\u001b[38;5;241m=\u001b[39mfs_page_size)\n\u001b[0;32m--> 562\u001b[0m     fid \u001b[38;5;241m=\u001b[39m \u001b[43mmake_fid\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muserblock_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfapl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfcpl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mswmr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mswmr\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    564\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(libver, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m    565\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_libver \u001b[38;5;241m=\u001b[39m libver\n",
+      "File \u001b[0;32m~/miniconda3/envs/mubind/lib/python3.9/site-packages/h5py/_hl/files.py:235\u001b[0m, in \u001b[0;36mmake_fid\u001b[0;34m(name, mode, userblock_size, fapl, fcpl, swmr)\u001b[0m\n\u001b[1;32m    233\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m swmr \u001b[38;5;129;01mand\u001b[39;00m swmr_support:\n\u001b[1;32m    234\u001b[0m         flags \u001b[38;5;241m|\u001b[39m\u001b[38;5;241m=\u001b[39m h5f\u001b[38;5;241m.\u001b[39mACC_SWMR_READ\n\u001b[0;32m--> 235\u001b[0m     fid \u001b[38;5;241m=\u001b[39m \u001b[43mh5f\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mflags\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfapl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfapl\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    236\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr+\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m    237\u001b[0m     fid \u001b[38;5;241m=\u001b[39m h5f\u001b[38;5;241m.\u001b[39mopen(name, h5f\u001b[38;5;241m.\u001b[39mACC_RDWR, fapl\u001b[38;5;241m=\u001b[39mfapl)\n",
+      "File \u001b[0;32mh5py/_objects.pyx:54\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32mh5py/_objects.pyx:55\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32mh5py/h5f.pyx:102\u001b[0m, in \u001b[0;36mh5py.h5f.open\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;31mOSError\u001b[0m: Unable to synchronously open file (file signature not found)"
+     ]
     }
    ],
    "source": [
@@ -258,6 +294,24 @@
    "cell_type": "code",
    "execution_count": 84,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/home/ilibarra/workspace/theislab/mubind/docs/notebooks/single_cell'"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# if rapids is available, use it\n",
@@ -270,7 +324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -307,7 +361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -325,7 +379,7 @@
        "      dtype='object', length=16918)"
       ]
      },
-     "execution_count": 86,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -336,7 +390,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -354,7 +408,7 @@
        "      dtype='object', length=16918)"
       ]
      },
-     "execution_count": 87,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -368,7 +422,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -395,7 +449,7 @@
        " <BarContainer object of 35 artists>)"
       ]
      },
-     "execution_count": 88,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -421,7 +475,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -430,7 +484,7 @@
        "(16918, 228259)"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -441,7 +495,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -456,27 +510,44 @@
     "print('here...')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Number of cells and peaks to use. Lower number for faster training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# n_select = atac.shape[0] # 500 # atac.shape[0] # 1500 # atac.shape[0] # 6000\n",
+    "n_select = 100\n",
+    "\n",
+    "n_sample_cells = n_select\n",
+    "# n_sample_peaks = 250\n",
+    "n_sample_peaks = int(n_sample_cells * .5) # atac.shape[1] # int(n_sample_cells * 1.5) # atac.shape[1] # int(n_sample_cells * 1.5)\n"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(8161, 8459)"
+       "(96, 50)"
       ]
      },
-     "execution_count": 91,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "n_select = atac.shape[0] # 500 # atac.shape[0] # 1500 # atac.shape[0] # 6000\n",
-    "n_sample_cells = n_select\n",
-    "# n_sample_peaks = 250\n",
-    "n_sample_peaks = int(n_sample_cells * .5) # atac.shape[1] # int(n_sample_cells * 1.5) # atac.shape[1] # int(n_sample_cells * 1.5)\n",
     "\n",
     "n_cell_types = len(set(atac.obs['celltype']))\n",
     "\n",
@@ -490,33 +561,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "celltype\n",
-       "Ngn3 high            926\n",
-       "Ductal               864\n",
-       "Imm. Acinar          796\n",
-       "Prlf. Ductal         738\n",
-       "Mat. Acinar          723\n",
-       "Ngn3 low             699\n",
-       "Fev+                 692\n",
-       "Fev+ Beta            600\n",
-       "Beta                 474\n",
-       "Alpha                418\n",
-       "Fev+ Alpha           396\n",
-       "Epsilon              241\n",
-       "Eps. progenitors     202\n",
-       "Fev+ Delta           176\n",
-       "Ngn3 high cycling    139\n",
-       "Delta                 77\n",
+       "Alpha                6\n",
+       "Beta                 6\n",
+       "Delta                6\n",
+       "Ductal               6\n",
+       "Eps. progenitors     6\n",
+       "Epsilon              6\n",
+       "Fev+                 6\n",
+       "Fev+ Alpha           6\n",
+       "Fev+ Beta            6\n",
+       "Fev+ Delta           6\n",
+       "Imm. Acinar          6\n",
+       "Mat. Acinar          6\n",
+       "Ngn3 high            6\n",
+       "Ngn3 high cycling    6\n",
+       "Ngn3 low             6\n",
+       "Prlf. Ductal         6\n",
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 92,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -527,7 +598,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -540,16 +611,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(8161, 8161)"
+       "(96, 96)"
       ]
      },
-     "execution_count": 94,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -571,7 +642,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -581,12 +652,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x500 with 1 Axes>"
       ]
@@ -596,7 +667,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x500 with 1 Axes>"
       ]
@@ -606,7 +677,7 @@
     },
     {
      "data": {
-      "image/png": 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0trbC6/UyMkucEYTDYfj9fq6EJBIJO1ZRe4sG76KsSjgc5iyfWlbRaJSrIMr+w+EwpFIps97FikOtVkMikXBLi1BaiUSCqyeNRgOj0QidTodgMAiTycTVlFqthslkglQqhU6n4yDi9/vR2tqaJrVPMxlxKyVJvdC9J6NKxm63sxqBxWKBy+VCbm7uUdwWl8sFq9WaWY38L7DMzCVj/78yctj0se3p6eHAodVqYTQaOWsluRClUslQVaC38ui7wfDHms/nw86dO3HcccdxG2nfvn2ora1FfX09Dh48iIMHD6K8vBxr1qw5SjBRr9fDbDZDrVazBD8p+ebl5aGtrQ1utxvNzc1ob2//waEzLcIaMGAAlEol9Ho9tFotsrKyYDabYbPZIJVK4fP5+FpJpVIoFAoYjUZoNBqEQiG+flQd0Gzj0KFDGDBgAKRSadrfVSoVFAoFv34ymeT2XFdXFx8ftQpjsRhMJhPPe0gHjNpq8Xgcer2eGfgSiQQulwt+v5/baZFIJO35FFwAMAiBAhDQi8LT6/XIzc2F2WyG2WyGy+VCfn7+MUEURUVFrFiQsV/OMpVLxv5PWzAYxGeffYYJEyZwdi6S30pLS3/ya/6cwJJMJtHY2IgDBw5g//79eOWVV7B79240NjZyFSRacXEx5syZg/LycpSVlTEcmGYLlGEDvQ7SZrOxkw6Hw/B4POjq6kJVVRVWrFiBjo6OtNefPHkypFIpNBoNAoEA6urqeM4QCARQWFjI7TC9Xg+LxcKcmaFDhyI3N5dRX1QdEO+FpO1VKhXsdjtycnJ4dqJQKJCTkwObzcZDeqVSyTDmUCjEgQMAS7oQv4bIjySmKZVKebYUjUah0+k4EaA2G+11AY7MbPpWLWJFQgvUgsEgV2ginLmvZYiU/xrLBJeM/Z+zVCqFHTt24Msvv4RarcY555zzs4LIL2VffPEFzjnnHBaAJJNKpRg9ejQUCgX69++P/v37o6KiAjNmzEBBQQE7yMOHDzMkOBaLwev1IpFIcHZPLHilUomcnBx4vV7ec1JcXIzrrrsOu3btwpo1azhgTJgwATabjSuRQCAApVKJrKwsyOVybgvSDMbtdsPtdrNMCg3gxbkFGTlvuVyOnp4erlqUSiVvuqSAmEqlEA6H0d7ezm00ggxTxUjtN5fLxYGFkF8GgwHxeJyhyoFAABqNhoMmBSkKONTeo0D494wIlrFYDP379z9mcBFJnBn7ZS0TXDL2f8aqqqqwefNm9PT0YOzYsbj99tv/T/TCCwsLkZOTg2HDhqGyshKVlZUwmUwoKSlJQ4pFo1GeBZDTbWhoQGdnJ4BeZ9fT08ODdJ/PB61WC61WmzYj8Xq9PE8hZzp69GgMGzYMGzZsQG1tLQwGA7PZc3JyWK8rHo/D7XanIaXUajVyc3ORm5vLMxIAXHGJ8xya21CbjKoOIi0SXJhacUajEZFIBPX19WnyKlTpUCVDgbWv0jEAJlVS0BBRcFStEnAgGAymBTB6Pwo0CoWC5feJA6NSqZCXl8fHQCaXy/8hGTZjP98ywSVj/3HbsmULHn74YahUKrz44ovIz8//Tx9SmlVWVqKhoYEZ4CqVCo2NjWmtmZ6eHkQiEQC9bSafz8cs+FAoBJVKxYguyvCJl0GESr1ej66uLkilUhZyFKsKlUqFU045BcCRNpBEIoHT6WRtrWAwyPDhvoNuANzKslgsXEEBYNY6obK0Wi2TEalFRlBj4roQhNjr9cJsNvM8hI6fEHYUmKgaoqApos4IMBCPx5l0SdeWZkLU2iLEFwVT8VypxUVS/DSHCofDKC8v5xYcBcdMS+xfZ5ngkrH/iKVSKWzYsAGPPPIIrFYrHn/8cYwcOfJnvx6tw/1HCK6fY5FIhKVbyNE6nU528IS28vv9sNvt7OC7u7vh8/nQ3d2Nnp4ebguRU6WBNv0MGzYMDQ0NUCqVUKvVUCgU3DoidBX9WK1WGI1GzuxpSO73+7naoSG4yF6nykF0+DQUF/koRLCkCoaqEbVazUu+AoEAOjs74XA44PV6EY/HefhOAYaY9oRYI4mXYDDIMx96T3ovOi6CHYs8F4VCwTwVnU7HaECCPFN1RCg3g8HA6s4+nw8ul4srLyJcZuxfYxm0WMb+7bZmzRo8//zzyMrKwt13380cip9rVVVVmDVrFoYPH44vvvjiJz+foMGpVAo6nQ5GozFtedX333+PcDiMVCoFj8eDqqoqlqvv7u5GS0sLO1iyoUOHoqqqiv9Nmb7RaEReXh7PI9RqNdRqNQwGA3Q6HVwuF0OUKcum9yVYMlVICoUCo0aNglwuh9lsRnZ2NkwmE3Q6HUwmE5RKJSsHSyQSbhER74OkX4iEqFAomChpt9uZv0KtP7lczjL4NCjv6Ohg2DAFIApGRqORlY1JxoUCBiUD1PKiYERtK6pSxA2UJJQJgK8fCWOKWmUkiGkymZCVlYXs7Gw+fwrUZDk5ObBYLGls/4z9MpYJLhn7t1ljYyNuu+022O12vPjiixg+fPg//ZobN27EGWecwcq4Xq+XtxUmEgk4nU74/X4AvfBdi8XCDsvv96Ourg4ej4cJej09PXA6nYhEImhvb8f+/fvh9XpRXV2Nrq6uo4b6NpsNDocDEokEZrOZnVVZWRkKCgpQWFiIrKwsRnJR0KL2DtDrSEm8kvafSCQSRlDR40g+ntpfdrsdyWQSLpcLDocDLpeLRSJzcnIYkpybm4ucnBzk5uay0xZ1uMQWndVqhVarxZ49exAOh1nGhmYxFIBo9kKAAUKDAWAGvcFgQGFhIasmezweDmb0WHp/qlpEaDH90DET8qyvppk4+Cd3RtdUp9Ohf//+abMWi8XCc6doNJoGhTaZTLDZbJlA8wtYpi2WsX+5hcNhPPXUU3jttdfwwAMPYP78+X+3101kOuKp/BAH4b333sP8+fO5jRKNRrFq1Sr069cPjY2N2LdvH8LhMLetaCZAu0Z6enrgdrtZGLKwsBCHDx8+6n2KiorQ1tYGi8WCwsJCWCwW2Gw2ZGdnw2g0Mk/FZrNxpi5WA+FwGEqlEsFgELFYDAqFgp0sSZyQOjBpeVFGL2bw5OAJsUUZtyiBQkNxQnzZ7XZ0dXVhx44dzHy/8MILmVBJVQM5eSIeUotJ5L9QcKE5CKHhCFZM/yaOCs1haMZC1ZIoOklBSYQUk06YKKJJr19QUAC1Wo1oNIquri64XC5u8QHpFU4ikWDQAe2kAXrVkGkHDC1OoxkOqTcXFhb+Mx/5jCETXDL2L7bNmzfj6quvxuzZs7Fr1y6YzeZjPo72iTgcDnR3d3NPXSaTQa1Wo6ioiCuOWCyG2267DYsXLz7qdc4991wAwOjRo1naXjRia4tGGl9WqxVDhgxBXl4eCgsLUV5ejkgkApPJxFBoQmKRcKM4VA4GgzwHiMfjPKgmFBitBKbNi8ARpBZl1iqVive6EBu+r/UNJJThizwVjUaDZDKJoqIiFBQUsCNXq9UIBoN8zOFwmAOLwWDglpVer+fNkGKQiUQifL2I1U/tLjouGtrToi9KEkgXjOYv1AYTAwl9FsTgTEAA0jAj6HAkEuEZlgh8INVpvV7Pe1yoDUfXmJSg6R6I5vf7EQ6Hf5JmXMaOtkxwydi/xILBIB5//HEEg0G8++67GD169N99PO0P6erqYicp7tv4/vvvsXfvXqxcuRKrVq36QY5DQUEB7r33XoTDYVxwwQXsvPV6PTQaDbRaLdRqNYxGI+LxOLq7u7lFQ4Nnk8nEjyH+hsgAFzN5clikCEAqvvQ4kkSJx+O8q4TQXCSfQg6a4LPAETgvBSgKOHScYoARr4XYzqHWj0hqJPAAvQeJUlLgpqrKZrMhEomwPAvNbqgNRjMqEdVGrxGJRCCTyRjMQBWNRCKB0WiE1+vlACsO8+lY6X0o6BKSjRZ/KRQKdHV1HZO8SlWe1WrlViOtJKD7Eo/H4XK5uGqk1xctFAplgss/aZngkrFf3FauXInXX38dCxYswJQpU/7h4wOBAHw+H2fCNFNoamrCunXr0Nraij179sBgMGD69Om4+OKLMWbMGADAtm3bsGXLFrS0tADolaEfNGgQO3Bqi0kkEuj1el6oZbPZ0NnZmaZ4TM6H1HZpaE1aW1qtlts0feG01LaiTJqqCnoMLbNSq9XciqJKIJlMcmYOHKlm4vE4AwCIB0OwY3pNcs5EVpTJZDw/kMvlPKCn2Q210Og86FhkMhmTK+m4yIi4SRUSBVARik2vJQY3jUYDn8+HWCzGga7vumGR60K/E2HWdF/o77R8jKoOjUbDgAJxjmQ0GmGxWPh89Xo931elUslACYvFcszP5P8FftX/3y0TXDL2i1lDQwPuu+8+jBs3Dh988MExWzrHMhq4JxIJ+P1+fPXVV/jyyy9x8OBBSCQSTJgwAc899xwuuugizkwNBkNaxulwOPD++++z7phUKmXCIrWEKEio1WqEQiGWjxczeaA3sw2Hw2mDYmr7EFiAnDEdjzgf6Mt4Fwf39B7knEUiIDlnIi7S4L29vZ1bcFKpFCaTCTKZjCsLChx0vYn9TrtPKPunykw8V3LcNKMxmUw8m6L2lThYF1cU9xWVJA6Q2L6k3xP5kR5PGyXFahA40qLqG7zosclkktWN6V7RnIbuAXFcFAoFGhsbebZEKs1GozFNc66vkUxPxv45ywSXjP3TtnHjRtx3330YNmwYFi5c+JNJkBKJBB6PBx988AGWLFkCr9cLk8mEuXPn4qSTTsLAgQMRCATQ2trKw32Hw4Hc3FzmtcjlcowbNw6DBw8G0AtVpeyfHA4AlnGnjF2tVvPwmoiCYtZOLHOSPqF2FTkyqlB8Ph+ju9xuN/M4wuEwS/fTDzl5ctBZWVmMrsrOzubzUqlUrAFG1QQ5eiI5ksMVfwAwIisajTJxUwwUNMeg60/nStcDSIcDU3UmIrpE1JnYsqMgKvJJiDhKQY7umRi4+rY6Rea9qDFGwYMUpen/xZ01TqcTDocDfr+fqxWpVMryOKSWLK4koHMuKCjIkCt/AcsEl4z9bPv+++9xzz33YM+ePbjnnntwzTXXHNWnpkE30DsDEPvmhNh56aWX8OyzzyIUCqGiogKTJk3CiBEjoNVqoVKp4HA4oNPp2JEAvY6nq6sLOp2Otau0Wi1XQRRQSKSS+u8A4PF42Em63W7Y7XbOyLu6uhjtpVAomKNBZD1iwrtcLkaZiZXKgAEDUFtbe9S1IpkUEc1EfydtLmrlkZWWlsLlcjGkOTs7GzabjZFMfa8HVR0UZChIijwXcXZEAYbaYnStiA9D5EwKMGI1Ib43nYsY2OjvdCxi8KGgQfMd+rcYrOi59Pr0p9iSpPOh96DqlDTF6PHEg1GpVDxrycrKQklJCXQ6HQKBAMLhMORyOYMaMvbPWya4ZOwn2759+3Dvvfdi06ZNWLBgAT766KNjwoXJcdOX3+PxsCRHNBrFJ598ghdffBHt7e2YNm0a5s6dC4PBwOS+WCzGGxUpIxaNhv5WqxVerxfbt29HXV0dmpqa4Ha70d3dzTvoc3JysGPHjmOeDzmYvjZo0CAcOnToqN+Ts9NoNMjKymI+hV6vR0FBASorK6FSqRhAYDAYeEEVDfBF+C212siJK5VKuFwudHV1oaurC+3t7Whvb8f27dvh8XgAAIMHD0Z+fj7KysqQl5cHm83GpERqhZGqMDl3sYUnQn/FwEOoKr1ez7tOtFot7HY7zywIaSY+DzhSPdD7ioRKQmiJmmFiQBHba2KA7ft7CsgE8RZhyzRnEltoIm+Grrvf74dGo4HdbodWq+X7l7Ff1jLBJWM/2ZYtW4bhw4fjjTfe+EFocTAYTNvv4XK54Ha7AfSSKR9//HG0tLRgypQpuOeee5CTk4NYLAaXy8XDablcDpPJBLfbzTwJoJf4dvDgQezduxc1NTXYu3cvB4H+/fujrq4OABjOrNPpoFQqMXDgQK6Y8vLyuJog/gm1vuj9LRYLpk2bxhkxEQiJWS8im8hxEzKLWkhA+pCahsnHklyJRCIsOW+1WqHRaNK2VCYSCbhcLnR0dCCRSKCjowPff/89GhsbkUwmUVJSgnHjxqGkpARGo5Gd7LEcNlUbYuuLkGAiTFmlUqGoqAhGoxE7duzgdh45b3HW1LfqoOE9XTtxTgSAUXjEuRErGABHHTv922w2w+fzQa1WHzW7oapJbBeK2mV0rqFQCDU1NUgkEigrK8sM8P8FlmHoZ+wXt3g8jurqajgcDv4dSaW8+eab2L59O8466yzMnTsX2dnZDEsVyZPUrsnJyWEH0dbWhjVr1qQFE4VCgZEjR2LcuHGoqKhAbm4uAPDQljgzRAAk55KXlwetVgun0wm73c7HGQqFeAe9z+fjdbriNkdyxGLbhhyf2CIS2eKUbRsMBgQCgTTVYgo0pCNGMxlqaxESjX6fTCY541YoFIzeOnjwIPx+P/bs2YPu7m7k5uZiwIABKCsrg9ls5vejY6HXppYhvY7okDUaDYYPH45EIoEdO3bwY2iOQ69H10Tk34gBlGRk6HG0y76v+xFRc3SNKSiQdA1VU5RwEHGT5kuEFqMWLAVQSiBI80ylUiE7OxuFhYUYOHDgv+Kr8D9tmeCSsX/aotEoCxdSS4dWDKdSKbS2tuKDDz7AF198gZKSElxzzTUYP348w0fdbjc7IpJFJyIcAHz77bf45JNP0N7eDolEgkmTJuH444/HlClTMHv27LS9KE1NTbxYK5lMwul0IhgMMvKLZjQUcBwORxq3hGYgdF7klESUFD1WrEZoOA0c4bxQC4hQSaJsiQhlpiBF0GEagPcNXkQ4lEh6pfZJHSCRSKRBr2lnSldXF+rr69HQ0MBV0IgRI3itMAVEMcAQdFehUMBkMqGxsRGTJk1CNBpl4Ufi7tD7iMfYd14htt/UajWys7PTBu19kXV0blKplIEW9NriPhlafOZyuXhGRteQ7gUdD3F7xKqGKlSDwQCj0YiJEydmWmO/sGXaYhn7p8zr9aKzs5OdCC25Iuf95Zdf4pVXXkEikcBll12G2bNnQ6fTMV+ChBaJNU5OoKamBnv27MH69esBAOXl5bj11lsxc+ZMlJSUID8/n1ftklF2a7VaWRuMHKAYMEgUkgIAKQATT0QqlTIySxRVpD+BIy0amm+IXAzKyGUyGbfQJBIJD9sp4xbnHUDvsNvn83GLRmxB0fUkB0nVA7Hp1Wo1c3ro+PLz85Gfn4+pU6dCIpEwb4gUmClbJ+g1ASBEsUrizajValZ8FmccdKx0HmLridplQK/Tj8ViTGgUA6eY39LriARVEU1G1RIRVI/VWqR2Gb0nteYIDk3Bi9QHgsEgvF5vJrj8wpYJLhn72ZZIJNICC9A7a/H5fKipqcFLL70EiUSCqVOnYs6cOTzUJXhuOByG0WhEQUEBOjo60NnZiT179mD16tVobm6GVqvF3LlzcfLJJ2PatGlpnASfz8faVeSwCO0Uj8fR1tbGbREKFJTZk2Mm5xUMBvm1xbmBOHgXAw1w9LrdeDzOAYcyZxHSK8JwKVgQR0VEYlHbS+R/iMNqek0KclSRZWdnc3VExEyxKqCZTHFxMRKJBKqrq7FixQrk5uZi/PjxPAtSKpVcecnlcgwaNAjbtm3DzJkzuaqMx+McKOn8+wpQymSytOBI7apjocLEBWV0bcXHiW0yOne6N8RjUiqVzLiPx+Ow2WysjExIw927d3MSQS1SujYi+i1jv4xlgkvGfra53W7emKhSqeD3+1FTU4O//OUv+OabbzBq1Chcc801rKxLHAVyHAQGCAQC+Oqrr/DOO++gq6sLFosFV111FS644ALk5eWlzW7I4vE49u7dy/MaWv4UjUbR0dHB8GfKyskZi1kzOTEgnUwobkyk/xchteRI6Tmi3AtVMnSe9FrEzRBhvn0VloGjWesiMVM8nr7ZejQaZZFPUZtMnH/Q68tkMlRWVmLkyJEIhULYv38/lEolRo4cCaPRyBk+0Itk83g8rMdGFUTfFhMdh3hdj8XapyAj8nxE7g0F0Wg0ylBlOg+RxEkJAp2bqLRAQZqEPX0+HzQaDZRKJatOkwIDcXsyUi+/vGVmLhn7WeZyudDQ0MAIMJfLheXLl+PDDz+ExWLBxRdfjBNOOCGtXUJzFHJKFosF69atw5tvvonDhw8jKysL5557LubNm4esrCyWU+np6UFOTk5a5eJyudDa2gqz2ZzWGiMdL5o9eDweHkKLffm+wpGiMxY3SorcDfoh5xcIBNLIksSBIRn4ZDLJDpDmPcTdISdKEvKksNwXGEDHQRWJOMOgSiCZTPJOGBqUizMikdsiilsSgovaRwcPHkQymcTw4cO5CiT5/M7OTgwcOBCRSIRbn3Qt6D3ofen4aNAvkhspKMrl8qOOlZ5H1RMdl6hmIAZqQqORBhu12NRqNWvJ0X1SKpVcyYoClrRmurKyku9Bxn4Zy1QuGftJlkql0NHRgdraWu6hb968GcuXL0coFMIFF1yA4447DnK5nBdbUbZO2bXZbMb27dvx1ltvoa6uDlOnTsWzzz6LiooKdkjUyyeUlxhY6H1Fvgap3EYiER6K0/4Qyra9Xi96enoQDAbh8XgYDUYquMFgkGcyhDwqKSlBU1MTO00ysUXV10pKStDc3HzU70m08Yee09bWBqvVynL+WVlZyMnJQWFhYVp2T5WAGECoTSQGRHEu1LeCIaOAbzQacdppp6GlpQXbt29HcXExbDYbBwa73Y7i4mKel1FwoIBLFRYFDrpeIkmSWocKhQJmsxlKpZIDjFhRihUgVRfBYJDPgypA+jxSgBXfi6pLUbE6Go3CaDQimUwy5DqRSKCwsBDxeJwh3j+kN5axn2aZ4JKxn2QdHR1obm5GKBTC119/jTfeeANtbW2YOnUqzjrrLBZKJGcHgJE5MpkMW7duxccff4zm5mZMmzYN119/PYqKipCdnQ2LxZK2D55QUCSvQrvbKWBRECJiXCQSQSgUQlNTE5qamtDW1sYQ6O7ubuTn5zMHpq8RFBcAO0CNRgONRoPi4mIOcKTZRVUOtVREjgxVGdu2bUvbD3PzzTcjJyeHs3+/389bJ2UyGex2O68N3rt3L9xuN4qLi9HR0cEs/cLCQhQVFcFisaRVCQB41kJ2LDKiaBQM1Go18vLyEAqFoFAoMHHixLRWH5FaiaciXnuqSGmQT0GkbyCkKo4qGHL6er2egwtpv1Fgp+qJEHHUIhOrIWL8SySStD00Xq8X4XCY50c0O3O73dDr9TzYt9lsaS2xnp4emM3mo4Jwxn66ZYJLxn60hUIh+Hw+VFVV4bnnnsO+fft4rkIKtEQEpKEvOert27fj/fffRzKZRG5uLn7zm9+gtLSUCYvUmsrJyWGVZJVKBaPRCL/fz+0tWllLhMZUqnch1v79+7Fr1y7s2rULGo2GVZKB3mBBGxmzsrJYV0qv1/N2SpLYp2BFUGDRgSuVSm5riat9RUmTaDSKb775Bhs3bkzbfgj0ys6QvhVVJUToS6VSqKioSHPItG3SbrejtbUVra2t2LZtG6v8VlZWYuDAgejfvz8HDtJUo0qF0FV0HcgIxWYymTBw4ECYzWbs2rWLn9eX1U+BlFqGpBsmSq+IIpzirEqcbVEQIKVkkdNDGl8UqOiYiUxLsj3iDhgRoi3Oc+jffTkzpLdGgAq6d2SJRIIRZxn75ywTXDL2o62trQ0PP/wwtm3bhkgkgj/84Q8YPnw4tzaIFyLOCpqamrBq1SpUV1djzJgxOPHEE5Gfn889c6lUyoP9aDTKUin0b2ojkcMIhULIysqCz+fDypUrsX79euzfv58dW2lpKYYOHYpx48bBarWioKCAgwdJrxAqivg0pDlG3AuSg/F6vezwxGoskUhwW6W7uxt+v5/JmH8PdbRo0SLWHqPhu06ng0ajgU6nQ0FBAXJzc1FYWMjwYlpPXFlZyVl4R0cH2tvbYbfbsXLlSvh8PvTr1w+DBg3CoEGDeMe9KMgoan/R31UqFfLz82G1WqHX6xmccSyCqMVi4VlTdnY2t8WoiugrBdM386fXUavVzEWxWq3o6elhyZl4PM4kSpLBSaVSDFKgRWzUBqRAR8kJVbyiIKdI5qRKmu4f3VOTycTkWyAjt/9LWSa4ZOwfWjwex5IlS/CHP/wBarUaV199NQYPHsx8EXJi5AyAXkb+xx9/jF27dmHQoEFYsGABD4SJd6DVanl4CxzJrAmqLM4IqGXy7bff4m9/+xs+//xzGAwGxONxTJ8+HaNHj8a4ceOg0WiYNEltHspm4/E4O2W32w2Xy8WPVSgUqK2t5V334owlKysrbU0uWVlZGRobGwEczdc4lg0bNgzFxcUoKSlhscRgMMiVWXV1ddq1pNnLpEmTUFBQwMPskpISlJeXM3/Gbrdj7969OHjwID7//HMMGzYMI0eOxODBg3lWRW0tyvgJiutwOGAymdDd3c0zir6ETa1Wi9zcXHR3d2Po0KFpwAh6jqjhJQYYGuBT1Ud6Y1qtFjk5OXA6nQx4oOOkSodQgCQYSq9JQbYvXFu8B/Sa4uPFqlBsJXZ1dUGv13OgP5YMf8Z+umWCS8b+rq1btw633HIL6urqsGDBApxzzjlwOp3sjMQvNZHc3nnnHXR3dyMSieCGG25ARUVFmtyIOPQNBoO8Npd65tFoFHa7ndtf8Xgcn3/+OdauXYuWlhZYLBZceumluOCCCzBkyBA0NzfzALe7u5v5DjT4JecnkUjw0UcfYc+ePUedZ05ODrxeL7PISR5Eo9EgJyeHEUokRmk2m7ktR+rB1Eppbm7Ghg0bsGfPnrTW2IgRIzBq1ChGWNHxkYKvRCJhdYPOzk50d3fDbrentd0oMAPgP00mEyZMmIAJEyYgEonA4XBg+/btWLVqFYYPH47p06dzIKIAk0j07s6Ry+U4fPgwpFIpjEYjBzx6L7pfNpsNVVVVGDp0KB87rW+mKlSsjMhoRkLnTPeFVgro9Xp4vV6+z8ARZB6dN6HrKKBQZUWBi+6xGFgoYREDndgyFCVr/H4/fD4fB3Tx+Rn7+ZYJLhk7pnV1deHJJ5/E008/jQsvvBCrVq1CSUkJPB4PHA4HpFIpBwOgdz7w6aef4quvvoJWq8U555yDadOmIRgMMlwXOKKTRRkiaWPRUJgWSwUCATidTqxbtw6bNm2Cz+fDoEGD8Lvf/Q6XXnopOxxqATkcDh4KE3ubslNygGVlZZg1axZKSkpYx4syVmKzUyUhyreIO+TJuVN7R61Ww+v1MjEvkUigpKQE8+bNw5lnnoldu3bh66+/ht1uZ0CCCIsl6DQ5SrPZDK1Wi9LS0mPeF5E/QlWD2PpRKpUoKCjA2WefjVAohL179+K1116DTqfDKaecguzsbD4niUQCv9/PIpzilkwKmuSIqaJyOBwcEEXOC7W6RPKkOOuh/xORWiR/r9frkUymLwGjAEWvTwoEfVdB03GK14Z+R1sqxepYDNLUHgwGg+ju7kZOTg4cDgd6enpYm44Wh/UFQ2TsH1uG55KxNIvH43j++efxxz/+ESNHjsSjjz6KqVOn8v+nUilUVVWhu7sbXq8XHo8H3333Hd577z34/X6cfPLJOO2005gx3trayv1xMbvUarXQ6/XIysqCwWBgKGtnZyfq6+uxcuVK7NixAzKZDLNmzcL06dMxZMgQmM1mFBQU8PEUFBQwv8PtdmP//v0sPim2b2iWQ9VBIBBgyDHt+rBYLIjFYgiFQmlbKEUeBnCEY0KaYclkknXQlEolO0Ea9pOeGIlS0nFQhi4uvJJIjmiZiXML0VFTlUaLz2geRUNsOkbK1hOJBBoaGrBz506YTCZMnz49TTXAarVyC5HehwAL9D4SiQQHDhxAZWUla5DReyuVSh6U9w0uBAQQ1zCL+l8kOknBls5V1GXT6XSQSCTcQqPgIm4JpdkPVakEWKDzp/tMJgYv4sVUVFTAarUyYtFms3GAyc/Pz8jD/ETLVC7/w0ZOTyKRQKfT4auvvsKNN96Inp4e3HLLLZg+fToUCgWcTiesViuAI/pd2dnZ2LFjB9555x3s3bsXkyZNwnnnncfIJYlEwrIq9IWnXj4A1sWiTZLhcBhffvkl1qxZg+rqamg0GsyePRszZsyATqdjglvf9bPRaBSdnZ3wer3cXiEkExll4DSwp/MlR07HS4AEcrIiHJeMHOaxGPyEJKP2IFU85BDFrYhiZUCvR9VH35ZO38AGIM2Risx1kQxKf5fL5SgtLUVpaSmqq6vx7rvv4sQTT0ReXh4jqOh8qDKjoEivTe9J7S9R5JJabnRdgSMac/R6BIQQP0MUwOhcqVVKwYmCEUn40PWmlpgYyOh9xJkKQZ1lMhkcDgcHQzovqpwJQt7V1cXEXaC3GifJovb29jRUXsb+sWWCy/+oORwOOJ1OhvI+9dRTWLVqFc4991zMmjWLs10SLBwwYABsNhu6urpQU1ODZ599Fps2bUJ5eTkefvhhZGdnM8dBq9UiEomwFpXosPv243t6erBt2zZ89tlnnDHOnTsXM2fOTBM/JEdLw37KaB0OB4LBIK8Wph9azEWOnhwdBSC1Ws3HSfIwRPKkquVYA2oSOiRSHh0zIctIpVen06GrqyuNtQ70ggC6urpgNBr5h9pz/fr1Q35+fhoEuK/mlhg0KJiQ0xSPlR5PQZYQeIMGDUJRURG+/PJLGAwGTJ8+neVeCEJOgZHulbiWmQJAIpHgNqDf7+d7LCoR0/F7vV4ezgPgioVmIAD4OvVVFhADNHBkNbKIJkulUryAjrZO0v0CAKfTydB14hfRtdNoNFxZxeNxeDwerozE+5ZMJuHz+TgZytg/tkxb7H/QvF4vOjo6kEql8Nlnn+Hhhx/Gcccdh9mzZ8NkMsHn86U9nqQ0pFIpVq5ciWXLlkGhUGDevHmYMGHCUdv/SJHW7XanKQRTVkqPra2txbp161BfX4+ysjKccMIJGDhwIMvJU9ZKjoyIilarlYmMZrMZTqcTABi95vf7oVQqYbFYEAqF2JEQV4ICjFarRTgcRmtrKwdbp9OJnp4eeL1eHvRaLBa0tbUdtYY4Nzc3bSEacWUMBgNsNhvPgCijphkNkSjFH3GoX1BQwIiw4uJirhKIiEpfWfo7Kf72JS5SRk5bG0U9LgA4fPgw6urqcPrpp0OhUECn00GtVsPj8XBbT+SbNDQ0oLKykisKCjp0XHSf3G43owKB9DXFqVQKGo0m7TXo3omBmAKjOKQHwLMt+j0BLNRqNc/p6DXputP7ElqNVmHTigNxOR3J8dDnXgwmNpsNWVlZP+s7979omcrlf9BoBfB9992HrVu34pprrsEJJ5wAt9sNr9d7FEfB4/GgpqYGH374IQ4dOoTZs2fjpJNOYkkXALyrxO/3QyqVwmAwpDkgsaVRXV2N9evXo6WlBf3798f111+PIUOGcNZJ2TP18pVKJc80CA01cOBARpVRG4n0pSjY0epiaqO0traiq6sLu3fvRktLC7q6utDW1oZwOMw8D9EIhpuXl4eysjLWoTIYDLzWmJZ26fV6mM1myOVybuOIAUHcCUOvLQ6gCcjQ0tKC5uZmtLa24rvvvkM8HkdlZSVsNhuGDx+O4uJifg3i6YiBgIbxVEEQuZFAA+J7V1RUQKVS4fPPP8eZZ56ZJr0vtsXIyVMbTqlUcjCk6pB2vNDj6LrTNRCrwL7ERpoVie09umcGgwFyuZxBAGLAocqGkGtERgWOyM5QIkNoRfEYaOGZx+Ph/TV0PlKp9KgWLH0GM/bjLBNc/scslUrh/fffx0MPPYTy8nI8/fTTLElC7RBRjjwYDGLlypX4+uuvMW3aNFx55ZXIzc2Fz+fjlbUiKY2ceyQS4X51KpVCJBJBXV0d1q1bB5lMBpPJhHPOOQdlZWWcYZIzpGBBA2KCBYstNRIjDIVC7AQkEgkMBgNSqV6hxb1796KhoQF1dXVoamqC2+3GiBEjsHfvXgCA1WpFv379kJeXh4KCAlitVh4OE3ud2mV9HSMNxKl6IAQVta7I8VGrTZzPUIUnzm3ImQ0ePBiDBw/mlmBnZye6urqwd+9efP3111Cr1Rg0aBCGDBmCAQMGsDMUOR6igyV1YLon1EIjpztgwADY7Xbs3r0bo0ePTpv/9CVeEhoNAO9CocpA5JXQeYnKzWJ1S0atNGqriUEHONIqozkOJR5iQkOIsEAgwKACMbiJQY1kdqjKoaBPVbH4WS0pKUmDI1OFlLEfb5m22P+IRaNRtLW14ZFHHsFf//pXzJ8/HxMmTIBEIkEoFGLNLuCInPvu3buxfPlyyGQynHXWWRgzZkzasJt69YSCooBCDoTaC7Q7pKGhAePGjcO8efNQXFzMcwviP4jyHFqtlmGqFoslTVIdAMOHOzs72XnX1NRg27Zt2LZtG3w+H9ra2gAAhYWFqKiowKBBg3jlb3FxMSoqKtDc3AyJRMLVBglaAuDKSdy4SE5VRHP1zcb7zkooeIqzABF6fSyj4EJaXBJJ73bK/fv3o6qqCtXV1cjNzYXFYsGkSZOQn58P4EjGTgNrAGlEUkoCxOomlUrho48+wrRp0xgCLQpi0r6Uuro6TJ48GQC4StDr9XC73fD5fHwvgSMSLnQeIlGWUFx6vR6BQAAulyvt/UTpGRK5FJUT6LVpzbNSqYTD4eAdQeKsRExWRC6SmBgRUjArKwtms5nVkukaGY1GZGdnZ4b5P9Eylcv/gPn9fnz//fe47rrrIJVK8dprr8Hj8XAL4w9/+AP69euHCy+8EAqFAg6HAytWrMChQ4dwwgkn4IQTTmAUFAWRvgQ2amuQhIdcLkddXR2WL1+OmpoaTJw4EXfccQcGDBiAUCgEp9OZps8lDqi1Wi2MRiMr1IrtNzJCX+3atQvr1q3D999/D6/XC7PZjEmTJmHy5MkYO3ZsGscBAENfpVIp7ynpOwQnB0dVDAVUgh4DRxBcNC+hoESKBTS7IEdPAYdmQfn5+cjKykpDtYkmMuppt4lSqcSoUaMwbNgwxONx1NXVYcuWLVi8eDH69++PyZMno1+/flw90DXtC3cWUWh0zU8++WR89dVXKCoqSnOiIh/I6/XCYrFAJpOhvb2dj53Y/GJApUpKDMJKpfIokU8AHPh+iEXv9/thsVi4mqJKlj57UqmUxS7FYwCOtMGowqPH0H2m74DX6+WNm2azGTabjQUs+7aJM/bjLBNc/sstlUph5cqVuPHGGzFhwgQ8+uijvK42Fovh22+/hdfrxa5du1BcXAylUom//e1vKCgowO9+9zsm3ZHDIkdN6B9xdkC/93g8WLt2LRobG5GdnY0HHngAlZWVfDyE9iIyYTweh8/nY0RPdnY2tFptWpZNLaZYLIZdu3Zh69atOHjwIBQKBYYMGcKV2KhRo2AymVjgsqmpiUUviVxJkGGa5XR3d/PMiAbRNH9yuVyQSHplUnw+H9xuN//fD+2BB4B+/fqhq6vrKEen0Wjg9XrZ2VNWnJOTg4EDB6a1Y+i6ilBuep5CocDAgQNRXl4Ou92OrVu3YtmyZTCZTJg9ezbLtNBr0P2je9n3uEjrrKGhAcXFxfyeJPFiMBh4IE7/praVOFPr29qi34nBhiraZDLJrUgCAdBzKMCJFRy1vIg3RUFCJOmKJoIRADBZlHTIROUGAo50dHQgLy8vraWXsZ9nmeDyX24vvPACbrvtNlx77bW49tpruT9NbahVq1ax0/nkk0+gVCpx0UUXYcyYMTzLIDgvDdcJQkqIHnJ48Xgcmzdvxtq1azF06FBce+21yMnJgcVi4fmF0+nkNhyhvQgqrFKpYDAYGJmm0Wh4Idf+/fuxYcMGbN++HbFYDAMHDsT999+Pyy67DPn5+fD7/XC5XNyjFxdOmc1mmEwm7u2TIGYoFMLBgwexb98+NDU1oaenB01NTUcN9gcPHoyamhqGDmu1WmRnZ/PyL0KvUQtHr9ezUgA5SurvUzByuVxp6wBqa2tRXV2NcDiMQYMGYejQoRgwYAC3GUU+CDlccrRZWVk4/fTTMWPGDOzatQubNm1CXV0dTj31VJ4NAUcQVz/UCR85ciS+/fZbFnEUmfriwjVRmUGUYaHPlMjZAdIFJCnAZ2VlIZFIwGAwIBqNcgUpwrDFuZbL5eI10YSQo9cVg3HfmQ4do8jZEYEUfYmXLpcLXq8XAwYM+Ge+dhlDJrj811oymcTDDz+MhQsX4qGHHsKYMWNQVVXFS65isRi2b9/OMF4yqVSK/v37A0gnttGaWLlcDqPRiEAgwNpUMpkMe/bswfLly6HVavHb3/4WY8aMQSQS4ZYKQYFF4hwRGYEjgoUAWAHX5/Phs88+w7Zt27B//36YzWacd955zMUhmGh3dzev4aXnezwelJSUIDs7G52dnZBIJOjq6sI333yDr7/+Gk1NTWhpaUnL5gcPHoz8/HxkZ2fzT2lpKYqKilBcXAyfzweXy8V7QiKRSNpSKnJcNJMRUVriYFwqlSInJwc2mw1Dhgzh94/FYjh8+DCqqqqwYsUKAL2BbdKkSSgvL09DhImZO/1ep9Nh6tSpGDt2LD799FMsWbIEF1xwAWw2W9pw+4fMbDbzAjCRTU/JSHt7O69CVqlUzC0i2RiqLOhcxT8pINJsi4IH8WmoEqKgRUAJkRBL/BOa7VBAp9UOIohADKQEXxarQDG40H2h1+7q6kqr0jP28ywz0P8/Yq2trSgqKvpFXiuZTOI3v/kN3nvvPbz22muwWCyw2+3sNEgr6u6770ZHR8dRzx88eDCuvvpqaLVazh4pCGg0GpbMP3ToEDo7O/HGG28gEolg1qxZOOH/rTYmWXZyvpS1d3V1IRQKMWpHhIQSv+Xw4cNYu3YtvvrqKwDAzJkzcd5552HKlCncpzcajcjPz0ckEmFl4r7XoKurC4cOHcKGDRuwefNmtLW18erb0tJSlJWVoV+/figsLORlZaIiLs0brFYr8vLysHPnTm6T0TUhmXpxcZZYIYn6ZMARIiT1+sWAQUaZdX19PUvtJJNJTJs2DcOHDz9qSZYIuRWH+d988w2+/PJLnHXWWRgyZAg7+7/3ld+7dy8UCgVGjBjBszPaHrp161acddZZkMvlaVs96TX7clLESo2uD92r/v37pykIKBQK3vhps9nSlAEoqaHgFA6HeV0BzeMILZZMJjkI0WxQDE40u9u/f3/a/hxSDSCu0rhx4zBkyJBMa+yfsEzl8n/A2tvb0djY+E8FF9q+F41Gcc899+D999/HBx98AJvNxpsjyZHLZDJ8+OGHaYFFIpHAZrOhf//+GDFiBH/x1Wp1ml6VTqeDxWJBT08PVqxYgdWrV2Py5Mn49a9/DYPBwA7MZDIhEAhArVanOVlixYuMePrS79q1Cx999BHa2tqQl5eHyy+/HOeff/4xRRzpS9+3hUX21ltv4bHHHuOKZPr06dzqE7kP4tIocXYik8l4pa/X60VjY2Ma/JoqEmKpi2KSpJZMxynqqhHvg1o/fVcTEzJLJpNh1KhRGDt2LCKRCHbv3o2vv/4ay5cvZ7RbNBpFKBRih6vVanHNNddAoVDAZrNh5syZKC0txdKlS+FwODBt2rQfDCwUpMrLy7Fu3TpUVlZywKCqjLJ6qjgIOSeqFYvZPs2vQqEQWltbIZFIUFxczIFDhDDv3r0bZrMZ/fv3T+P+9CWP1tTUYMiQIUycpGtFQUPkztCshu4NVXrimmwy8X5JJBK0trZCr9ejpKQkM9D/mZYJLv8HbMuWLZg3b97Pfr7f70d7ezu3wlasWIFXX30VNpsNTU1N7MCAXqe5atUqrFy5EjqdDqeffjqvGaYMUYSNxmIxJo9ptVrk5+fjyy+/xDPPPAOJRIK7774bQ4YMgd/vR09PD3NNyOk7nc40KCpBjCORCNxuN3p6erBx40Z89dVX8Pl8mDlzJu655x7MnDkTGo0mbeGVaEaj8e9ekxNPPBHl5eU466yzWCmAtmiSaCUtliKoLhE/iTgokfTqo9F8hn6oJUU7SkghmeZRQG+wNxgM6OjoYDSUmNGL6Dhxo6Po5EjqRK1WMwJu48aN+PDDD2G3248657KyMnaEFLj79euHm2++Ge+99x5sNhsGDhx4zOtF72swGOB2u9kZi4GDgii1VYkjQgGWWlUUZLq6utDZ2QmDwYCKioqjeCf0s3v3bpSXl8NqtTJREgAjFMW5D5Edw+EwfD4fB2+6jrQkjXTiqOIRyZriLIiM7rkIg6b3aGxsxNq1a3HddddlxCt/gmWCy3/YNm/ejGnTpv3s5xNhMJlM4vHHH8fy5cvx8ssvo7y8HG1tbWllfywWw1tvvYUtW7bg9NNPx/z585mISIqzAJiF7nA4kEqlYDAYuBJ57rnn8P777+OKK67AlVdeifb2dng8HoRCoTQpepHvQCgpADyzaWpqwieffIL169cjlUrhlFNOwZ133omxY8cy78XlcqGtrY1nMDqdDjKZDFarlV9Pr9fD4XAcdV2KioowbNgw1gojJWQRcUTOhRyl3W5ntJZWq+XWlt1uRyAQQFdXF+rr69He3s6bH6PRKMu605DaYrHA7/ejs7OTB9LE0QCOVAk0B6CARvMAAgSIxEca4E+fPh2DBg3CE088kbYrRqlU4uSTT06bY1GrR6/X47zzzsMrr7zCK5aPZeIgXYQr04yIAo3H40mTd7FYLCypQ/dpz549UCqV3HoSkW5koVAIBw4cwKhRo6DRaLhaEZn5hAxTKpXYu3cvxo4dm0bcpOsvk8k4GNHSLyJc0p8iyIAqFBHlSMRc0pbz+/04fPgwHA4HOjs70draCpVKhaKiIm69ZeyHLXOF/oNG2lVEgPs5RrIbS5cuxaFDh/DnP/8Zo0ePRktLC0tiSCS9cuXvvvsuGhoacN9996GsrIwRWZQp0rpioFcihmCjUqkUy5cvx9KlSzF48GC88847KC4uZnQWZfmE9gJ6BSaJj0DZvUwmQ2trK5YsWYKvvvoKqVQKl156KebPn4/c3Ny09cfV1dXo7u7mfnpHRweysrIwevRohkcD4GyVAqxUKmVJFlqH7Ha7AYDhyJR5i20tMeuVy+U4dOgQ6urqsHfvXtTW1qK9vZ1bLyaTCSaTiVtpNNynwEcrkmnwT7tIRGkWsQVHiCdq0clkMtZWs9lsvBogmUzC6XSioKAAt912G5599tm0tQBWqxVarTZNWJIG3gaDAWeeeSY+/PBDXH755WkikmSkhkCDfVoPTZyjYDCI1tbWtFlRPB6HyWTiwFJdXQ25XI7Bgwcz54eORzzveDyO3bt3Y+LEiXzeVPloNJq0HS9SqZTVD8ThPM1+aIBP17vveYnDe7G6EuHS9CcFos8//xwnnngicnNz04IQba4sLCz82d/Z/xXLBJf/kKVSKXz++ec499xz/+nXWbduHRYuXIh7770XJSUlqK6u5i9JLBaDw+HAn//8ZxgMBtx6660oKCiA2WxmyC9lf8SOF0lvNTU1WLp0KQKBAG666SbMmjWLv/QiK1uEJAO9bRlillutVkQiEbz55pt48cUXIZPJcNFFF+Gss87iwS7Q6/x1Oh0OHDiA5uZm5i+QQ+7s7MTWrVsxefJk2Gw2FuDs6upi3g5l/bm5uaxJRY45HA5z1kuZK4EcAoEAqqqqUFVVhYMHD6Krq4vnNVarFUOHDkVeXh5yc3Oh0+nS2oyicwsEAtzPp/4/XVdqOVI2LjpoOh6pVMqD5ZycHE4O6HUIKFFQUIC2tja8//77AIBx48bhxRdf5NUHNI8QB9rl5eUYNmwYvvjiC8yZM4crFRFWDvRu5ezq6kJBQQFLngSDQdTW1mLEiBFpbaVYLIbu7m7k5+ejra0Nra2tGDFiBOx2O6O4qJKlQJxKpfD9999jzJgx3Lai+RclI8lkEh6PB/F4HAaDAdXV1SgrK4NMJoPP50tbtkbtOkoMxPtKVR/dJ/G+iXBxsaJRKpVwOp3HJJ4CR5KwTPXy9y1zdf5DtmzZMpjN5n8ajXL48GH8/ve/x6WXXopJkyYxsY/UgAOBAP70pz9Bp9PhxhtvhE6ng8lkgtFohNVqRXNzMzweT5qDkUqlcDgc+PDDD7Fr1y6ccMIJOO+882A2m3lOQgxrcZZAMGdRJFCpVGLz5s144IEH0NnZiVNPPRXnnHMOsrKyoFar0dXVhaysLB5y19XVweFwpA2r6bUpkO3duxcjR45ET08Pz1PEeQcRI4nsmEqlGHFF75NKpdDW1oaqqiq0tbWhuroaQG87bcSIERg0aBDKy8vZiYnOhZwOOSfgCMDA7XanEfD67kMhB0qOWYTPis9RKpW8oVOtVkOtVrMaAFUi1157LZqbm/H999/j/PPPh1wux9/+9jc899xz+P3vf8/sfCKnRqNRHHfccXjrrbfQ2NiI/v37c4CjobhUKuWFWV6vl68JrY+mYxQrLwJsbN++HePHj0+TgemrnyaRSLB//34MHDiQrwOdu1arRTAYhM1mS5PgDwaDsNvtGD9+PN9TEQTh8XhYgoiutdg2oySKghgFFjFIUjuQrkMsFuP5Sl/SqXgvM/bDlrk6/wHzer1Yu3YtXnvttX/qdTo7O3HWWWdhypQpOOWUU+ByuRAKhfhL63Q68fTTT8NkMuHaa69l9V6TyQSDwcB9cpVKxRv34vE4PvvsM6xatQrjxo3Dww8/jNzcXNalIqQQoYUoGCWTSWZwU6ZNSsp/+9vfcNxxx+HRRx9FaWnpUbtCWlpaoNVquZ1E/BBqvxCiiEh0drsd+/fvR25uLkKhEJYtW4Z9+/bhvvvug1KphM/n42Ol46F1xAcOHMCOHTuwe/du+P1+6PV6jBgxAjfddBMqKipgsVh4nkAbEsXBr+iQyFnR0B0AD5mlUilrX4nzCapM+uqMkWm1Wl4BTedNSgU+n4+Dkd/vh9FoxF133YVDhw7BYDBAqVRizpw52LVrFx566CHccMMNyMvL42tBTvWCCy5Im6uQIyZnrVKp0NLSwgHQ7/fzvEgkcYrosFWrVmHs2LEAjsxpRDQYvQ8N+EtKShgNptFoIJP1LvQ6ePAgz66o2iPoNfFPxOCVSqVQXV2NcePGpb03tdJaW1tRWVnJFZREImH1B5EjJG4VpZkbrQbo28YUddsy9sOWCS7/AXv88cdx1113/SSIIzlc4o2Ew2GceeaZyMnJwe9+9zukUil2ANQSe/LJJ5GdnY2bb74ZKpUKJpMJZrOZt0C63W4eXiqVSjQ0NOCVV16Bw+HA2WefjVNOOYWdsiiRQS0OItCRYyVHEYvFsG7dOrz++usoLy/Hgw8+iJkzZ/LMglBgxJSXSqUwmUw8CyFRRHofUYiQHJvX64Varcaf/vQnfPTRRwCATZs2Yfjw4dDr9VxtHD58GDt37sSBAwc44JWUlGDGjBkYNWoUbxekuYkIGQaOsL5FRyz+H1UZ1Baz2+3o7OzkhWK0YyYYDCI7OxuxWAxWq5VBEtnZ2dzXVyqVMJlM7Gy1Wi3q6uq4bUkzHppxkYTJoEGDoFKp4PF44Ha7MWrUKOj1ejz//PO44oorkJWVxTMKukcUpOh6kgI1Od9wOMxrlHt6etJIiSLBkCqPYDCInJwcnvMRKktEwPl8PnR2dmL8+PGMriMEVzKZxLZt2zBmzBg4nU4OfvF4HG63myvZY81UROvb9iSoMgUXCmhUQYpBlWD6er2ehTW1Wi28Xm+aBpzVas3wX36EZYLLv9n27dsHlUqFioqKH/X4RCKB7u5u+Hw+dnRmsxm33347srOzceedd3JmSE7H6XTi2WefxeDBg/Gb3/yGe/1ms5llPCKRCA9bnU4nPv74Y2zatAlDhgzBtddei5KSkrR9FmJPGkDaF9Jms7GUB6HWtmzZgpkzZ2LOnDlQKpWor6+HzWbjrX/U7qCASQ6BskJCc4lORNyLLpVK8cc//hGrVq3i///666/h8/nQ3t6OAwcOoK6ujgfOAwYMwLBhw3DTTTdh2LBhXFFQQBH794ROEucp4kyEzp8WjdXX1+Pw4cNobW3l51LWTeAC4sz09PSgpqaGlYABoLS0FP369cOUKVMwfvx45ObmQq1Wo6OjI40kSdebZkgdHR1IJpMoKipiPgktDcvJycH8+fPxxhtv4Pjjj2clADKqYqjyEispvV7PWl0/pNosAhO8Xi+GDh3K6gT0pxhYJBIJ9u7diylTpgAAXx8CHQDg9QnhcJiTqEQigZaWFuTk5HBgoGB/LJ4KfUbFAX48HufNoxRg9Ho91Go1VzDiOm6SDyoqKuJ1zd3d3VAoFLBYLLBYLH/3O5uxXssEl3+jpVIpPPnkk3jppZeO+r9YLIaenh6uPgwGA7KystDR0cG/A3qd2osvvoj33nuPN0KKSBqJRILHH38cMpkM9913H2QyGZxOJ9RqNWfStBhLr9dj//79eO+991BbW4tf//rXmDRpEgBwlkhIL6puaGkT9bx9Ph+USiUKCwvhdDpx3XXXIZlM4qabbsKgQYMA9AZIr9fLaCqS9CDHRnwSq9XKX24SGSTZfJ1OB6PRyHDnl19+OS2wAL2w7s2bN2PQoEHQ6XS46KKLUFZWhpycHB6ck3w7ObdUKsV7QcQtluQ8Kdun/npHRwd2796Nnp4e7Nmzh+9VcXExTjrpJFRUVHBLhe45IfLonMRhc3t7O1wuFxoaGvDyyy9jyZIlmDNnDs4991xuO1K7kob7lJFT26mlpSVN2JEqV61WiyuuuAJ/+9vfYDAYkJ+fz45UnCVRsKbj1el0aRs8RdBBXykbkuI/44wzuPqhQCgGl/b2dhQXF6ftChJ5KLT6meY39LlIJpNobm7G8OHDuRISg0HfmRcFY7o24vUidCRVJiTGSeep0Wi4olQqlTxzUqvVSCQSKCwsZNHNjP1jywSXf6O9/fbbOOecc45aOkSyGG63O435THBgsQTft28fHn74Ydx+++3Iy8uD3++HTCbj1tXjjz+OQCCABx98kJE3CoUiDXnl9XphMBiwY8cOPPLIIxgyZAj+8Ic/ICcnB8lkkvdbZGdnw2Qyoa2tDU1NTQCOOAxqjdCX/K9//Ss2bNiAQYMG4aKLLkIymUxro9GcIJVKIS8vD8AREhuhyiKRCIMGSJAxGo3C4/EA6A1SNTU1eOmll44pWwMA559/PiorK3mORL17aqfQUN9kMrF0PA2ETSYTdDodnE4nfD4fz67a2tqwe/du7N27Fw6HA3q9HuPGjcNtt92GsrKytH02VIlRNSQGF3JwNJC3Wq0oKipCJBLBaaedhhtuuAGdnZ3YsGEDLr74YvTv35+lW0TnSSYGE8r+CQAhIqZmzJiBjz/+GGeccQbvKaHXEdUKSL6GhtrknClgEMmWHkcbOXt6ephsq1ar0dnZmTbAl0qlaGpqwgn/TxpIrMao7fndd99h3LhxaYGJZj5ut5tbdGIQpNaZyM3pG9yo2i0pKUEqlUIwGERXVxevl04mk7zGmz6rTU1NqK+vRzgcZt5WIpFAZ2fnMdUiMnZsywSXf5N1dXVhxYoVPB8QraWlBVVVVQiHw+y0TSYT93apPeVyuXDLLbfgjDPOwIwZMzh7JZTM4sWLcfDgQdxzzz0oLi4GAO7LZ2dnIxwO85D6rbfewrJly3DBBRdw1kkZGu1QoWy7tLQU3d3dvAeFsnjKgJcsWYLdu3fjgQceQP/+/eH1enmpE3AEMUTtOzpHGnhTNphIJDiTpJadxWJBS0sLvvnmG2zduhXd3d1/9zp3d3ezsgBwROyQHCVl3uSsyBmLrTCVSgW73Y7NmzejoaEB27dvh9FoxMSJEzF9+nRUVFRAp9Ol7a6x2+08M6L3onYkzRYouKRSKR5SUxAjpnlxcTFuvfVW3H777Xj77bexePFiDB8+HOeff35aK8jn86UBFkSJHeDILIRAB7Nnz8bnn3+OuXPnpjl4CgIUjAkxVlZWxp8HCpx6vZ6JigQAoarZ7/fzsjhy1HQPqqqqMHjwYA4YFNykUim8Xi9XXaTeIKoX07nQZ11En4kzFTpW8X7T7+l4lEoloymp9UVVGQVov9/PgIGenh4mvxLMnM4xY//YMsHl32T33HMPHn744aOG+MlkEvv374fX601jXJNzJQn3RCKBBQsWwGKx4JJLLmEV4HA4DJfLhQ8//BDffvstbr75ZhQUFPDOFJfLxcgwm80Gt9uNhQsXYvv27XjggQcwfPhw5ocQXJWGlwqFgiVPcnJyWFKEHJnD4cDLL7+MUCiEF154AdOmTUNtbW0aHJcG5PRv2uFCGaDX62V1XdpMCfQ6zxUrVmDz5s1ob29HLBbDkCFDMHnyZJx11lnQ6/XYuXMnqqurYbfb0d3dzVWFKK1CGb2YqVNFRUgg4Mig+rvvvsMnn3yCHTt2QKVSYcaMGXjooYcwYsQI5ObmYvDgwejs7EQkEkFPTw/fL1J8JpImVSrk/CnrTyaTvEKZpHGoZUlOtaenB7m5uZg5cyZGjx6NpUuX4vbbb8fcuXMxdOhQPl46ZhEiTBk8nS+1/iwWC8aOHYsNGzZg1qxZrBBAJEmC/pIg6JgxY1BdXc0yPvF4nAMyBY5wOIyDBw+isLCQ51f0uaUgFAqF4HK5eCZDnysKsvF4nOcZNOOi86M/pdJejTuVSsU6ahS8iSRKSQFwhH1vt9sZhhwOh7lydzgc3LakzZWRSAQ2my1tdkOwZ2pJdnV1sVp2xv6xZYLLv8G2bNkCk8mEyspKRkgRaoek68XAAvQ6Qbvdzs7x448/hlwux/3338+ZN6GmNm7ciE2bNuHyyy9HaWkpEyFjsRiTC2lesmjRIjQ0NOD555/n6oYqBnKIlL1Go1FG/xDDnF7v8OHD+Mtf/oK8vDzcd999vLSKUEAENxarHJod9PT08KY/rVYLj8fDbZnVq1fj008/RU1NDaRSKcaNG4cLLrgA48aNg8VigV6vh9FohNPpZMkV8dpRpSAujxKRQvF4HIFAAN3d3dBoNFwZ7dq1CytXrkRHRwfKy8tx0UUXYcKECbwuIBgM8nwpJyeH1X0J3RYIBDh4UG+f/qSqxWg0cuvPZDJxsCVioBgAY7EYD/YXLFiAyspKLFmyBNu3b+fWKj2O5jg0G6KKkq5FKnVkL3xXVxf279/P4qRZWVmw2WxMDJTJZExGzM3N5aokHA7zNaSBPW3DnDZt2lHrqunc9+3bh7Fjx3KQo2pCqVSyqsGhQ4cwePBg/qzTZ6+vajS9PgUcg8HAn3O6TwB4pma32zFgwICjgjCRdem9CHwQCAR4jQMFWxFWLZFI4HK5kJWVlUGL/QjLBJd/scXjcTz88MNYtmwZ3G43uru7OTtyuVxpqCFCK4nwyAEDBmDHjh144YUX8OSTT/JAPB6Pw+VyYdu2bfjLX/6CU089FYMGDWIOCnBkX0oymYTf78fdd98NhUKBt956CyqVCjk5OZzlk0Pxer3cAnE4HNziAsDyKLt27cLq1asxcuRIzJ8/H6lUCi6Xi/eQU2VDjo+QSZSZqtVqOJ1O/qJv2LAB3333HdatW4dkMonhw4fj+uuvx9SpU3k+Q0aVQH5+PmQyGex2O5xOJzsuWgdMmbOIaiOkmkajQWdnJ9avX4/NmzejuroaI0eOxJQpU1jWn1pFwJFlaRRkrVYrksleSX+3282tR3KAOp0O+fn53L6hSkWtVrN8PyGigB9WdpZKpbBYLJDL5Rg2bBgWLlyIDz/8EO+88w7OPvtsWK1WHsiLpEKC2dJqX/q8JRIJTJ06Fd9++y0PsKklRUAKCiLUYiPOCbWVyOja+ny+tMfTOZMUSzweR1FRURo/RuT8AIDD4cC8efNgNpvR1dXF/0ftOApq9H3SaDRplQfdK2pdUUvM4XBg/PjxjNQjQEVnZyerHYvnRLMxas3ScQQCATQ3N8NgMCCVSjHHKGN/3zLB5V9sf/7znzF//nxoNBo0NDQchc+nLz/1zQEw9l+hUKCjowMPPvgg7r33Xl4VDPRqitXX1+OVV17B+PHjMXXqVB6SUsCg1pDf78ezzz6LVCqFxx57DLm5udDr9cjPz+fKwuVyoaqqCm63G6FQCA0NDZxRUwtJoVDgu+++w7vvvou5c+filFNO4UBIlUIi0btYrLCwkLcLiuuAKfvct28fNm7ciA0bNiAYDKK8vBwLFizA7NmzkZ2dzWKRopH2lUajQXZ2NrxeL/Ly8rglQw5PqVTygiygt8Xo9XpRX1+P7u5ufPPNNzh48CDMZjMmT56M2267DSNHjoTBYEBdXR1aW1s5wIj3SeR3aLVa3m5J2TQdY3d3Nw4fPszrlSn7d7vd8Hg8yM/PR15eHkwmE3Jzc2E2m5kvRBUk0Ms8FwERWq0Wv/rVr7B371689dZbOOmkk1BWVpZWJRI3w2q1IicnB3V1dQCO6KpJJBJMmzYNDQ0NGDt2LM99qNoBeh02Delpp3wkEmHnTJ+vVCoFo9GYNmgX5ypNTU2c5dPGSRFwQNUNASxycnKY00IKEH6/n5MgCqR0rSiIkeK1mKSlUimuwCkwmUwmRKNRNDc3o7S0FC6Xi4+bhDMTid59MlQ9kfAnwaTpHDP2jy0TXP6FtmXLFnz22Wf48ssvObvua4RaIpQYtQKoH3733XfjzDPPxNy5c+H3+9HW1ga9Xo9YLIbnn38eBQUFOPPMMzkrpBYJcGTO8NxzzyEWi+Hpp5/mVlwymUR3dzfkcjmysrJQXV3N7HhqfZH0OlULmzZtwnvvvYdzzz0XJ598Mjsk+nJTSyGZTMJutzNHgLJ6pVKJb775BqtXr4bdbodOp8O0adMwa9YsjBkzhve4q1QqXh1MDo8WViWTSVgsFkYmZWVlIR6Pw263c2CRyWSoqalBY2MjamtrcejQId46OXbsWJSUlGD+/PncrqF1A7QITUSokRFnhYQ5Kfh5PB5UVVWhrq4OjY2NqK+vh9/vx5AhQ/iaWK1WRh3l5eXBaDSira2N50lEGpw2bRoqKipw+umnc/VHn41gMMhOu6ioCDfccANef/11tLa2YtasWRxcqHKj5MHr9XJwp+TFYrGgpqYGFouFeTM0mKcgLQZLUl/Izs5mkiyANKFIuu/AkW2btbW1mDlzJoD0+R2h0/R6PWprazF48GD+HlgsFv4ORKNRHDp0iHkldG+pnRaNRhEOh3kOQ5WpCGemyoySDVrzTcdPgBmpVAqXywW5XI7q6mq43W6GJtPahWAwCI1GcxTaM2PHtkxw+ReZ1+vFVVddhffeey+N1d3XZDIZCgoK0NHRkSYTIpPJ8M4778BoNOKGG24AAA4+gUAATz/9NJLJJC677DJur1BfmTJsv9+P119/HaFQCEuXLuUlX5RdkwM5fPgw/H4/twPIqVF2KpfLsWXLFixbtgznnnsupk2bxkQ0ygypx63T6dhh0RrgWCyGb775Bhs2bIDP58OoUaNw8cUXY9SoUfx8cXtjPB6H0WjkNh9pagWDQahUKrS3t/NsiGDWhw4dwoEDB1BTU4Pm5mYUFBTA7XajrKwMI0aMwJw5c9C/f3/OpKXSXul4OgcArPIsl8vR0dEBm80GnU7HiDBi87e1tWHJkiVYs2YNfD4fDh8+jKysLPTv3x/nnXcehgwZgpEjR6KkpISh4l1dXWntL4lEwq0VgrkePHgQ33zzDd555x0MHDgQU6dOxcyZMxnBJKLd1Go1LrvsMnz55Zd45513cPHFF6chpbKzs3kgTRptYtUxcuRI7Ny5E1OmTIHf72f4O80DCYxAn+VIJMJVBn02nE4nsrOz00AqNJ8QNdMoucjJyWGlCVIYOHToEObNmwebzQagl/1OxF6gF/1XXl7OMyx6DwqGFIgoGIlwZLF6D4VCLM0jtueoQhWXjomCrtS+JdmhzLzlx1smuPyL7Oabb8ZVV12FkSNHAuhtozidTgSDQR7gk9REaWkplEolOjo6OJsKhULIzs7GlVdeyU5DKu1l57/wwgvwer245ZZbmGMRi8Vw4MABrFmzBhdffDHy8vLw5ptvQqVS4dZbb4XVauXsru8WPhpIkwMS23OEoPrwww9x4YUXYsKECWmMdapaFAoFQ6kpuMTjcWzbtg1r166F3+/HmDFjMHv2bJSUlHCLguYagUCAqwK/34+SkhLOEEnEkTgatbW1+Pbbb/Hdd99hx44dcLlckMlkKCoqQnl5OY4//njk5+cjNzeXN18SF4KQRuQglEolw5spM/3666/x9NNP4/TTT8dVV13FbZX3338fX375JTZt2gS1Wo0JEyZgxowZGDBgANRqNQMtjEYjB1naS0PXlIwAAEajEQqFAv369YPBYEBlZSVcLhdqamqwZcsWvPnmmzjppJMwffp0ZrNT9i6Xy3HOOedg9+7d2LhxI2bNmsXzCa/Xyw6azl8U3CTRUEKBUfYvSviT86akhaoAkrtxOBy8uoGuJ83WqqurMXjwYGi1WkSjUW5BkewPVcQNDQ1MnqX7Eg6HkZeXB7vdDofDgRkzZsBgMPD5UICkQTwFRjpWajNLJBJul1GQp/kLAQ/o+lCnIBaLwWg08vdD5HMBvXNMu93+T63J+F+xTHD5F9gHH3yA1tZWvP766/w7nU6HRCLBEGKgdzjpcDhgNBpZJoTw/jKZDKeeeipniFlZWVCpVNi8eTNWrVqFJ554grM9UjWurq5GV1cX/vSnP2HgwIFoaGjAnXfeybIvpFklbndMpVKMWKPB+/3334/Bgwdj7ty52L9/P9atW4eLLroIkyZNglarZY4F9eUps08mkzxjqKmpwYoVK9Dd3Y0pU6bgxBNPhF6vZ6gsfcEJiqtQKDiTFwe2iUQCW7duxZYtW7B161Zs3bqV+QdDhgzB7NmzUVZWhoKCgjTyHP0pyqaT4xFBBsTMJxjzpk2b8MwzzyCVSuGbb77BkCFD8O2336K5uRkulwtTpkzBPffcg0mTJrE+G70X/Wi1Wg5sYoXY16itQ5Ip1PNXq9WYPHkyTjjhBHR3d+Orr77CH//4R5xyyimYMmUKOzuSqZ84cSKLoc6YMQM5OTloamqC3+/ngEBD90AgwEnNwIEDsW7dOowdOzYtqQCOMN3D4TBXoaSQAIAZ7Pn5+TyPonMgUu+wYcNYHp8AHtSyUigU6OnpgVKpREtLC7PlaTdMKpVCfn4+4vE4r0+g46AWH833iGRJv6Mg5nK5GAFH1TXt/qHkiK4L8XMI0EDJn0TSuzLCaDQiHA4zXD4vL+8naQP+L1omuPzC1tLSgrvuugsbNmw4aiBMDpQ4HTRnaW1tZcdMPWxaMBWLxdDU1IRIJIKOjg48+uij+N3vfoeCgoKj2NnEoo/H46iqqsLYsWMxYMAA5OTk8BektbU17ZicTmeayGBrayu8Xi927NiBxsZGFrGcNm0azyVI2BA4IhQIHOnPf/zxx1i/fj1mzZqF6dOn81xE3BdvNpvTBvY6nY7XI2/duhVvv/02tm/fjq1bt6KoqAiNjY0YO3YszjvvPIwYMQLl5eXweDzo6elJc4qiI+/bihS5J8CRFg5VcuvXr8fLL7/MzyONtlNOOQWXXXYZBgwYgGSyd88IOV+NRsPvT0P3AQMGoKCggBdLAWDnR0YVG1VkwWCQX4MqEwq8M2fOxODBg7FixQrs2LEDF110EWw2G0ONSaZn9uzZ2LBhA4YNG4bi4mIOJtSGIl4JAOZBkXAp3VcikxLEWkTbiRIwVAmKIpjBYJCRevTZpOeQmCUZqS1UVFTA5/OhpaUF/fv3TyNE5ubm8sI5lUqFgQMHIhQKpVX4Wq2Wj0ds+/l8PpjN5jT9tEgkApfLxTBkqgLpPIDeGaiITqOWdmFhIVpaWnio39DQwHI2GTu2ZYLLL2hNTU0466yz8MQTT6CoqCjt/ygDJGYwBQVR44kyKAA8+KTsqrGxEbfffjsuvfRSjBo1Ko1NTkiavuz1PXv24JFHHsFrr70GhUIBs9nMhETKBIl1r1arEYlE0NDQwMfV3d0NpVLJcioAuFVFQ2JyHlSFvfHGGwgEAliwYAHKysr4vAj6Sq9BTiwQCODQoUOor69HT08PvvjiC5bkmDRpEn77299i0KBBGD58OHNkiHNC1RiR4yirJidGrQ56L7VazZko6XSRpP7nn3+OFStWpAUnmUyGefPm4f777+e5CNAbGHw+Hz+G+vpUCbW3tyMcDqO0tBQGgwEej4edLqGlRI4G/T6RSMBkMsHj8TCkNh6PIxgMwmg04vrrr8eBAwewaNEiTJ48GWeccQY0Gg08Hg9XZMcddxx2796Nrq4uDB06lGdoVNVRxUbBiwAcdOwkbCq2mahSSKVS8Hg8DKSgioLuKc3sRDIpgDQejgiLrqmpwZw5c+D3+5kjRYGWqklKunw+H2uC6XQ6tLe3MxKR3oMqjXg8jnA4DJvNxp9bnU4Hn8+Hrq4uGAwGRq8B4LYcycSIbWNak03rIABw8G1vb8/IwfwdywSXX8g6Ojowa9YszJ07F+eccw7/nohmHR0dcDgczNQGwAq2X3/9NXbv3o3rr7+eh4zkbIDe9skTTzyBqVOn4owzzuD+N2VRRGrs6xgTiQSLQKrVarS0tDAkVnys2GJpampKyzBjsRgeffRR3HvvvejXrx9XV8S+Jqexbds2rF69GlOnTsX8+fNhMBhYRgXo/fLTkrLu7m6sXbsWe/fuxc6dOxEOh6HX6zF79mzceOONmDJlCsaMGcOosFQqhdbWVnR2dsLtdrNGWTQahclk4j4+OUtqzdB2TcpEaZOjVCpFdnY2HA4HPvnkEyxdupQrMVGbKpFIYPPmzQDSKyIKWKRCLErH0FbMcDjMII19+/ahpaWFWe3UvsnKykJ2djZvuSRnR5IoFHwoGw+Hw6ioqMDNN9+M+vp6BAIBvndUGaVSKYwfPx5VVVU4dOgQKioquC0koglJd6uiogKHDx/GyJEjeYunw+HgAEHzC1G/i9pTxC/Jz89n0EEgEEBTUxMsFgtXS2QiCo2CD7VpFQoFHA4HTCYTHA4HYrEYWltbeRZJOnEERtHpdPwdoeBOpE+fz4dDhw7BarWmsf7ps0cgDqrmCwoKGNosk8mQnZ2NoqIimM1mBozYbDa4XK40IAYh1TJyMMe2THD5BczhcGDWrFk46aSTsHDhwjSm8P79+3kQSUGjp6eHS/rt27fjhRdewC233JKmx0TPJ82wiooKXHLJJYjFYjCbzfylDwaDPBgVZT/GjBmDK664AlOnToXZbEZnZyfzTrKysnhNMUEtid1NvAgygmpS24zaETSUTSaT+OSTT3D48GFcfPHFmDVrFoxGI0aMGJG2MtlkMsHtduPPf/4z6uvrUV1djaFDh+Kmm27ClClTGA5MWk6tra1oa2tDTk4OD2wpm6bzJIdKjkEUWqR2BrUZqTpra2vDmjVrsH79etTW1mL06NGYNWsWRo4cCbVajebmZjQ1NaGurg7t7e3o7OxkVBoFHolEApvNho6ODm65hMNh7Ny5E83NzWhvb0dbWxvsdjtUKhXOPvtstLe3p0nSGAwG+Hw+VFdX49VXX4XT6URFRQX69++PgoICDBs2jGV/KNjRZ0MqlWLAgAEMq7VarTxLoSq2f//+qK+vR1tbG3JzcwGArxEFColEwpI/NMymOYa4P0ZsO1GbjOZ8BFkHwNWg3W5HaWkpo6/ononW3t6OgoIC/jdVn11dXVzNtbW1MTmSAid9TukcCKwgl8vh9/uRn5/Pc8Hi4mJOOGgWFo/HYbFYoNVqodfrUV5ejrKyMgYeEFrO4XDgxBNPZM6S1WqF3++HzWZLI14SICJjR1smuPyT5na7cdJJJ2H8+PFYtGhR2pDP7XZzJktOiVodyWQShw8fxrPPPotrr70Wp512Gme09AWOx+N4++23EQgEcNlll/HMwmKxwGq1QqfTobOzE7FYDO+99x4A4LTTTsP5558Pq9XKBD1CLNEXnFBnlG3rdDo4HA64XC52FBKJBCUlJTjllFNQWVnJVQQtv6IB/uuvv46enh5cc801KCgogEwmQ1lZGbq6upgn0NDQgOeeew7r16/HgAEDOOiNHz8eGo0GHR0d3HKgFghl+UReC4VCKCwsTGvvUG+d2OPklEhTjRQHampqUF9fj6qqKkilUkybNg3XXnstJk6cmLapkd6PpHkGDBjArTCgN/N1uVysI1ZdXY3vv/8ee/fuRXNzM8rLy2EwGNCvXz+cdtppKC8vx/jx43kdgcvl4naOQqHgjJnaP01NTdi1axe2bduGjz76CDNnzsTUqVPTKgDK/qllSovfKKioVCp2kOXl5aiqqoJCoWARVLFdRdeS1gj39PTwvIMqWVFAUvz8eDwenndQAKHA7vF4eBYlqiSIAaa9vR3l5eV8L+kcReJrfX09CgoKGFlHlRJVoWq1mv+PPpPU9goEAiyRQ0HV7/dze42kXA4cOAC73Y5BgwbBZrMxSIaCaU5ODnw+H/Lz8zlBIx4RtTozdmzLBJd/wr766is8++yzGDBgAF599VWuNGjI197ezk4TOCLCp1AocPDgQTz//PO4/PLLcfbZZwPo7e/269cPEokEO3fuxFdffcUqx6LiayQSSeOkLFu2DKFQCEuWLMGYMWOYAEiD1IaGBibhEbIL6G3v+P1+DBw4EDKZDI888ggAYPjw4Zg3bx4KCgp4lkGVAwU/n8+HJUuWIBwO4/rrr4fZbGbZeuqFNzU14bXXXsPatWtRWlqKe++9F6eddhq3OAj15fP5uHdO6roiJyQUCnGvv6CgAPX19RywKRMnhVutVouDBw8ywqu+vp4XhZ199tkoKyvDoEGDeGEY3Zeenh54vV5muGdlZUGtVrPoZygUgtFoRENDAxYtWoT169cjEAiwmOb8+fORnZ3N0GJyPA6HA06nkwO86NibmppQUVHBi7DKysowfPhwnHnmmXC73ViyZAnuvvtuzJw5EyNHjmTnTAkMzZDi8TgrFdB9Isj1kCFDsHPnThiNxjQEHnAkQOfm5qK1tRXFxcU8ixP1tKjios+URCLB2rVroVQqMXHiRIYDU5uKMntRF0ys5qVSKUuzkBYdDdWJdBuJRNDZ2YnKykqeSQJHtqzS+9jtdtZPI8CJ2+1Gc3Nz2v6caDSKaDSKxsZGmEwmbknL5XJ0dnZCIpGgubkZw4YN44G/eL50LwmkEAqFUFZWxt+ljB1tmSvzM23FihX41a9+hSFDhmDz5s3o6OhgxE9TUxMMBsMxyVYSiQQ1NTVYtGgR5s2bhwsvvBAAeOCu0Wjg9Xqxc+dOrF69GnfccQe3BghRRftPAGDv3r1YunQpXn/9dQwfPhzNzc08ZCeUDWlgkaSGRqOB0WjkvrNarUZVVRVqampw/vnn41e/+hWAXrRUIpHgbJ5Ucb1eL1555RXI5XJcddVV0Gq1aeKOgUAAq1atwubNm+FyufDwww9j4sSJkEgk6OnpgclkgsViQSAQQGdnJzo7O/mcXC4X8w7oegG9c6f29nYUFhamESjpy19VVYUdO3Zg586diMfjGDJkCKZOnYprrrmGyX8EfhADLL2HmLWKRtItr732Gl577TUkEgnMmTMHjz/+OEpLSxmBRAQ9moOQlAtdP+KliEbQdBKoFJF/UqkU8+bNw+TJk7F69Wps2LABp556Km8wFVtUIhFUFIWkQf/kyZOxfft2jB07Nq11Sq2w3NxcHDhwAMXFxczNoWqOqhIRIeZwOHDgwAEUFhbyimtamU0zD/HekYYXcVgoKNLMkKpNaj3S/Iugw1ShEciFQBEA0NbWxi0tUZ2C0GmkcACAwRG0gVJULaBr6nQ6MXjwYKRSKVZEJn20ffv28ePcbjfuv/9+LF269KjPTMZ6LRNcfoa9+uqr+M1vfoPi4mK899576O7u5oy0pqYGl19+OfR6PZYtW8aOkGznzp148sknceaZZ+K6667jikIcqm/evBmLFi3CbbfdhpKSkjRdrqysLMjlcpjNZjgcDjzyyCO45ZZbMGLECA5w4vs1NjZCIpFwVgscQXoRVLOqqgr33nsvrrvuOkyePBlOpxN6vZ6hwTRf0Wq1aG9vx2uvvQatVov58+ez0xBVY5977jns378ft99+O0477TQeuBMQgZx4JBLh4XU4HE4TIxRRTkRCVKvV8Pl8CIfDaGlpwb59+xgQEI1GMWzYMFx22WUoLCzkDJnaRTQvUSqVvLP+71kikcCaNWuwZMkSrF69GjNmzMAzzzyDM888k4f1breb4anEAo9Go7xCV+T0/JBCg7hOmCo4Qi7J5XKYTCbMnz8fHR0deP/999HZ2YnjjjuOz4sG/6RPRtBimjfRn5MmTUJ9fT0GDRrEj6GARBBlQuCJeltisKPP6MqVK1mKiOTyCY5M1Qu9PrXKxJXZ9Dsx+TKZTBwMRCgwnSdVUNRepuvtdDoZeUfS+SK6ja4vfX/oc0jfKZr10OzG6XRi69atrLWnUCjg9Xqh0+nQ0tKCvLw8Pq8dO3bwZzpjR1vmqvxEW7hwIe644w4MHToUX3zxBXQ6HUOAq6urccUVVyCRSGDhwoVIJpPIy8tDR0cHYrEYdu7ciaeeegqVlZW45JJLuMdN8hKpVAobN27E9ddfj1tvvRWVlZX8BSFIZktLC4xGI5RKJR5++GFceumluO2221BdXc0bFGkIT/BJeg/qa8diMXR1dfEXkEiBxx13XNpeFWpfEamRJPsLCgpw1llnAUDal/7QoUN48803UVpaitdeew2lpaU8wwHAK5JbWlqYF0GDdoIEazQauFwuVp6lXnlrayt6enpw+PBh7Ny5E36/H5WVlTj++ONRXl6OrKwsbgVRcKWWjOjkBg4ceMwKRTzGt99+m3W7Lr74YjzzzDPo378/S/V7PB54PB7EYjFeGUDZOc09qAohSX8gXfqfjg/oBYQAvYhDqVTK+moUJGmXzWWXXYZXX32VN1fStSM9LQpylDzQXIz2y9D/URUikhvpeESZeQoK8Xic113v2bMH1dXVAHqTlI8//hjnnXdeGgRZlF2h49PpdJDJZBwISkpKYLVaWU+O4PlU+blcLpjNZj4uEZhBVS5VyiUlJRyEADCAgK6zCPCgYEtzGAoqRG5WqVSQy+XQ6XSMSqTXJqIxAN6YmrEftkxw+Qn20EMP4f7778eECROwatUqyGQy1NfXw+PxoK6uDr///e8hkUjwl7/8BcOGDeMviFKpxOrVq/H0009j2LBhuPfee2EymdjZEx5/x44duPrqq3HDDTdg1qxZ8Pl83FKhDIu+8G+++Sa2bduGRYsW8VZEyi4pg6QMlngShK4hJVnaIpmXl4dZs2ahvb2dv4gU0MixhMNh/OlPf4JUKsUFF1zAzoOyxM8//xxbtmzBueeei3POOQejR49mp0pGLQaPx8OzFnLQNpuNOSHV1dWoq6vj9coUvEmv65ZbbsGYMWOQnZ2NeDwOt9vNfXNxYRTBvhUKBWw2GwoLCzFkyJBj3tvOzk4sXrwYL730EoYOHYo77rgDp59+OmfuHo+Hh/t0fV0uF0vni/ByoJfHkpOTAwCw2WyM5tJqtdyyoaBvNpvh9XrZ4ZHgJcFiCaWUSqVwyy23YMmSJVi1ahV+/etf8zZPnU7HlSVl9zSQ79vaqays5EAizq0oC6fKIBaLpW1D9Xq9WLNmDbeeAKCuro6fS46Xqg+qyGUyGUvzZ2VlobOzE8cffzzGjh2bJhJKsjTJZO86A1IiEIVYaQOmqIVmtVr5/ynRAY4oNIs6ZwC4QqZAQ+0/SrxUKhXq6+tZCZo+o+IcUK1WM8w9Y8e2zJX5kfbMM8/gjTfewNy5c7F06VJ4PB60trayhPuiRYsglUrx2GOPoba2Fs8++yzuvPNO+Hw+fPPNN3j00UcxcOBA/P73v+eBIM0qotEoqqurcc011+D222/HGWecwXvdfT4f72anjLiqqgqvvvoq7rvvPjQ3NzPpkZxcKBTiLwplsORoKBjI5XJ89tlnaGpqwo033gjgiEqsqHRLjuOdd95BS0sLbrrpprTXcrvdePPNN5GVlYVHH32UiYNWqxUej4ePheYAtLucBrfNzc04fPgwGhsbUVdXx3vKfT4fysvLMWHCBJSWlqK8vBwDBgxAdnY2V3PkNGmQT3MhCoYKhYLVfPPz848itgJAc3Mz/vCHP+DDDz/E2WefjTVr1mDs2LFpj0mlUszXAXqDCy0so1kKkUEJFUbSLk6nEzabDXv27IHRaOQfm82GvLw8TJs2DQUFBYy8ovYNzdh0Oh1sNhuDBYqLi/HMM8/gsccewxtvvIGbbroJADjLFhFZ5Iy1Wi0jyETlbbFVl5ubi87OTobvplIpnp/RcR08eBAlJSXIzs5Ge3s7D+b37duHwYMH8+PEoEItJzoWu92O3bt3Y9y4cbDb7UyEpSpTrVZzdSjK+dPAXy6XIycnh6uIYDDIChAUJEQAgShNQ59bGthTZUdBnRaa0XUJBAK8F4fUxMXvBRE+MyrJx7ZMcPkR9qc//Qm/+93vcMkll+CNN96Ay+XCwYMH4Xa78de//hVfffUVzGYzLrnkEkgkErzwwgtoamrC9ddfj/PPPx9ffvkl+vXrhxtvvJGz3mAwCJPJBL1ej3379uHWW2/Fddddh6uvvhpqtZqViskx0/KrRCKBV155BSeddBIqKiqYyEeQUAom4l4Xcn5iG+TgwYNYs2YNfvvb33KWStUPfdHp31u2bMG2bdtw6623siCjVqvF7t278dZbb2HgwIG45JJLeAFTdnY2Q4hFoie1u3bs2IEDBw7gu+++Yy2n8vJyjBkzBsOGDUNpaSksFgtnk/F4nIUu/X4/srKymDtEKCAanov9fLpuBQUFKC0tTZPqOHz4MBYuXIiNGzfiqquuwuOPP57GuxCNVuGKRppYGzZswIEDB1BfX4+Kigp4vV4YDAZkZWUhLy8PxcXFMBgMsFgscDqdsNvtqK+vx86dO2E2m/H888/DYrFgwoQJGDFiBIYOHco6dISCowVWRqORCbBPPvkkHn/8cfz1r3/Fb37zG3as5DxFJ0/XWCKRYPTo0dixYwdL2VDryGAwcFuOqhmaddBnKTc3F3PmzEE0GsWOHTswY8YMdHZ28nCcsng6bvpMUkUtyuoEg0E4nU44nU5ujZFeHQ3T8/Ly0lZRi0KTxAGiGRq9R09PDzo6Ong5G1X84o6XUCjEkGuawRDCja4b/UQiEZhMJphMJg74Go0GJpMJ2dnZ6OnpyQSXH7BMcPkHtnjxYtxyyy248MIL8Ze//AUA0NraCp/Ph9dffx1r1qyBVCrF3LlzYbPZUFtbyxpfzc3NeOqpp9CvXz/ce++93D+n/nI0GsV7772Hbdu24fLLL8dVV13Firr5+fmw2+2sT0VKyuvWrUMikcBJJ53ELTDK2kSIKbUOVCoVPB5PGqHR5XLh3XffxSWXXMJSKPTcvryEQCCA9evX44YbbkBRUREzkv/2t7/hk08+wRlnnIETTjiBlQByc3NhMBj4i01Z8p49e/DOO+9gy5YtCAQCGDZsGE4//XSUl5cjPz+fe/skc0/7b0gqhjLYaDSaJsNC5x+LxZgQSv9nNBpRWFiI/v3787nv2bMHjz32GA4cOIAFCxbghRde+If6UPTceDyOTZs24f3338d3330HlUqF0aNH44ILLuA2nVqt5lXM1NqiNoxonZ2dqK+vR3NzM7Zv347vv/8eL774IgKBAAYPHoyLL74YM2fOZIdGCCzaMtnT04PrrrsON910E3bt2oXx48ez1hmhqig405CdkFcA0sQyKXsnYUmRS0OmUqng9/sZ7EEzCL1ez9Uwtaxo1iLOvgjhRei7aDSKnp4eDmREwCS+itfrxciRI5lbRZ9rWmFN18Pr9bJEfjKZZOg4tdUIRk9Bo6enB36/n5UmKCiJagji/SbyJSUqJpOJ1btHjBgBu93O68Izlm6Z4PJ3bMmSJbjxxhsxb948vPXWW5DL5fB4PIhEIli0aBE2bNgAmUzGkGSSQelrlEmK5bdcLse7776LTz/9FLNmzcJVV13FO+IB8AeWlIdTqRQ6OztRV1eHiy66iLNJrVaLzz//HGvWrMF9993HO0loeJydnc0b/YDeTP+vf/0rTj75ZFRUVHCrhL6AwBFFXIlEAoPBgHvuuYezyEQigaVLl+LAgQO47777MGjQICiVSmRnZ8Nms/FMRalUIhQKYfny5Xj99dfR0tKCIUOG4Mwzz8SYMWO4t07vp9freSAPgB0dzVDi8TjUajXPJihbpZkKbW2kGQEpE5eVlQEAdu3ahWeeeQY1NTW46667cOaZZx4TKn4s6+7uxp///Ge8//77yM7Oxvjx43HyyScjLy8PCoUCgwYNgk6n49eTSCTMkwHAu0Tos0CKwnq9Hjk5OTjttNNw2mmnIZFI4ODBg9i7dy8ef/xxfPzxx7jvvvvSKipimGdnZyMrKwsvvPACLrnkEjz//PPIycnhIE0zHuKR0P2lTLy7u5uXcImoMZFLI6IAqcUFHOHHiJpuVAHR/huxlUWfe6qaqVVKbSlqx4rv6/F4YDab00AQ9Bp6vR5WqxVtbW0oKSlBTk4OV2kOhwNtbW08Y6HjEKtxquaojUyfazpX+g6Gw2FkZ2ezFpnZbEZ9fT2v0Whra8sM9f+OZYLLD9iqVauwYMECnH322XjnnXfSRBHvvfdebNiwAQqFApdccgm3GEKhEHbs2HHUa+3btw9vvvkmLrnkEv6ivP7669i4cSPOOeccPPbYYygtLT2K7SuVSnm5l0Qiwdtvv40TTzwRubm5LDsSj8exaNEiPProo1AqlcjJyeFMn3ba006LaDSKF154AZWVlTjxxBO5nUEcA8rU6EtG2SxlixKJBC+//DLUajUee+wx9OvXjxd70b4YmjksXrwYK1asQGFhIS677DLMmDEDsVgMHo+H4cRA+lCVdJwog6Sgl0r1yq+Tdhep/VIGSYNii8WCfv36ccUjlUpRVVWFhx9+GHv37sVTTz2FadOmMepLp9MxwqqvJRIJrF69Gi+//DK+++47FrDMzs5mbS3K+ltaWpCVlQWbzQaFQoGcnBwOLN3d3WlrFmivCQV1s9nMfBSZTIYRI0bgV7/6FW666Sbcc889OPfcc/HrX/8a1157Lb8m7c+JRCIoLi7Gww8/jNtvvx3Lli2DSqWCwWBAIpHge6ZWq9HU1MSw96ysLOzbt4/RWMcKIiLKToTCE0CEfk9Bi5Imeo5Go4Fer+c2l3hdCXIMHBn+02tTNRGPx5lUSe9N8yP6rpE8UDKZZAQgBQmqSPpCoqmSpqBDlV08HodOp2PdMKlUiqKiIhQXF6O4uJhJnaI8E1XnGTu2ZYLLMWzr1q2YN28errzySjzzzDNpTO5HH30U27Ztg0ajwZVXXomioiL+8O/duzeN4CVac3Mzs6mfeOIJ7NmzB7fddhuuueYa5OfnHxVY2tvb0d3dzYHl008/RXFxMaZMmcIDbK1Wi8cffxxnnHEGLr30Uhw8eBDBYJAHwWILwev14vPPP4fL5cIVV1xxFHmMUEyiDpSY2YfDYbz22mvwer246aabWACSSG+RSATffPMN3n//fWzZsgXHH388Fi9ejIkTJ0KpVMJqtWLnzp28yY/mP3RNCJlDO+nJUVEbjlp/JJ0ikvvEHjmtWu7p6cGjjz6KDz74AL/+9a9xzz33IBgMorGxMQ0BptPpkJOTw20ZlUqF999/HwsXLkT//v1xwQUX4IMPPoDX68X+/ftZlJIIqXT8drsdfr+fN2B2dnYiPz8/LbCQ0XWm+0StH3p/s9nMa6m/+eYbPPTQQ/jss8/w5JNPorKykqVNfD4fdDodpk+fjnPOOYfbfIRCE83hcDCIghytOPSWSCQsBil+FinhANIJrdFolO8hcEQuJRaLYf/+/bj44otht9u5zUbJinhcFKQApLWmCApM703zM2rT0jHX1tbynhXgSGARZY3C4TA/l85H3NhJwANShVar1aitreWWK3Bk2RqQvsZBVLnO2NGWCS597ODBgzj99NNx4okn4rnnnkuDGt5111149tlnMXToUFx66aXcV5ZIesX6Vq5cmfZa+fn5GDFiBCZOnIjy8nI0NTXhs88+w/79+3HHHXdgzpw5rFMkWjgc5mG+VqvFrl27sHv3bjz44IPQ6/XM2N+4cSN27NiBvXv3QqVSYdCgQbwbhoxIlAcPHsTmzZtxyy23MNeBHDj1n0U+geh4ZDIZli5dCofDgXvvvRdZWVlpKLQDBw7gd7/7HRwOB6ZNm4Z33nkHJSUlLMNPu0Io07NYLGnVC6GASP9Mo9EgKyuLs04aVqtUKlitVni9Xp7n0KBaq9WyoOeHH36IBQsWYMaMGVixYgWMRiMTNgOBAFcYQHoQ//DDD/GXv/wFY8aMwRtvvJGGGuvo6ODrk0gkUF1djZqaGhw6dAihUAi1tbVp9zA3NxexWAzTpk3Dcccdh6lTp3IbSqVSpS1sI+4GAFYm1ul08Hq9mDJlCj799FO88sor+P3vf4/rrrsOJ510Et+fzs5OqNVqnHvuuVyl/fGPfzxKKJIAEYROpAG2iAykATWtSqDA0PfzQO0pkehIVcHXX3+N1tZWtLe3s4MWocq000hEalG7jj5P1MYTZyJyuTxtBxK1wCorK9Ha2sogAWLPSyQS5szQMF/cL0PnTi2+VCoFnU7H1Vxrays/ToQxZ4LLj7dMcBGstbUVJ598MgYPHoxly5alBZbHH38cTzzxBLKysvDUU09BoVCgvb0dPp8PDocDixcvRjLZK2N+7rnnYsKECSguLuYh5/79+/HUU08hHA7jj3/8IyZOnMgf6L5tGXK65IyXLFmCm2++GTabjQfWcrkcL730Eh555BFejKTRaFBeXg6v18vIMdLwWrp0Ka655hpe80oZrIjAodYDMfLp/P/617+ipaUFd955J2/LVCgU6O7uxiuvvIJNmzZh5syZuO6661BUVIREIoHW1lZ2IhQYzGYz7+mgISz9PzkZav0QwZQyXqVSiaysLCgUClRVVaVJpZDTP3z4MC699FIYjUa89NJLabs2Ojs7maxI++BJ5PHTTz/F8uXLMXnyZLz88ssoKytj8qNMJuNNoXQsy5YtQ11dHQYOHIgZM2agsLAQFosF8XgcBoMBbrcbb7zxBnbv3o1PP/0Un376KaRSKUaOHIm5c+di6tSpKCkpSZMmoftNCsbE+aEFXNdffz2Ki4vx4IMPQq1W44wzzmDuRSgUQk5ODm688Uacd955mDBhAs4444y0zxQpThsMBiSTSQwaNAi1tbUoKSlh52swGNDe3s4zDJLepySEwAT0+aT7QoEimUzi22+/RSgUwkcffYSLL76YAwHNSqiyFNdK0A8JhhJQxWAwpCHWKJjRZ5Y+p263G16vNw0eb7Va046LPss5OTkMDCCSMAUXkVwKgGHIYuWTCS4/3jLB5f+Z0+nEySefDKPRiJUrVzK8MJVK4YUXXsBdd90Fo9GI999/H1lZWWhtbYVE0it38vzzzyMWi6GwsBB33HEHI7UoM1q/fj2effZZ6PV6LFmyBGPHjmXnLWawZJQdu1wuvPLKK5g+fTrKy8s5AEilUnzyySc49dRTMW3aNITDYXg8Hs6IiXzndDoRDofx/PPP44wzzkBhYWHaDgzKLEXWMuk8kfP/5JNP0NjYiPvuuw8WiwWpVAputxtffvklVq1aheHDh+OBBx5Afn4+o3EoEw+FQqyxRhWSUqmEyWRiaLXT6eQBdCqVwv79+7F//37s3r0b5eXluP7667mCcTqdLCZJw2IKkMuWLeP1z+eeey7a29v5/nV0dKCzs5OdE8F5N2zYgHfffZerLVJd7u7uZoUAALyaQKPRIBaL4corr+R7RYHKYrHwEi0AmD17Nrq6urBr1y5s2rQJmzdvxq5du9DR0YEHHngAs2fPxiWXXIJTTjkFPp+PwROUaEgkEhQVFcHtdjMv6IQTTkBeXh5uvPFGRCIRnHDCCex8pVIp86geeughjB07Nm3POwlqer1eWCwWKJVKbN++HRUVFXxdqNVK8zdy8jQTEVtbpOdFQAYKLHTdDxw4AKB3ERcBTIjES6gumqeJ70GfTZPJxIrIPT09LD1D8GO5XI6DBw9i+vTpacoCREJtaWlhMAGx7qVSKerq6jBp0iQASFMgp2oJALc9jUYj8vLy+H4AmeDyUywTXNDrIH77298y1Je+EHa7HW+//TYWLFgAlUqFp59+mjO6UCiEuro6PPXUUwgEAjjllFNw+umns3wH0AuT/OSTT/DRRx+hpKQEzz77LAYOHMitJiBdhpxMq9Wip6cHzz//PGpra/H0008z9t9qtaK2thZbtmzBxx9/DKD3A19fX8+7UChgmc1mvP3228jLy8O4cePSVI1pGCtqPJGjpkytvr4ea9euxR/+8AcenEokEnz99df47rvv8Ic//AFlZWVMyqTWRSrVu5djxYoVuPPOO6HX69HV1cV7OGioTdDTvXv3Yvv27TyzGjp0KE4++WRUVlZyq4OcPfX7qe323Xff4amnnsKkSZPw6aefYsSIEcyxSCaTcLlc8Hg8DKSgFuazzz4LuVyOO++8E4MGDWJEncfjgdfr5RaaVqtlMmtFRQXq6upY5UAikXBQkUiOLJEiy8/P57UFyWQSu3fvxvLly7Fq1SqsXbsWa9euhVQqRWlpKUaMGIFwOAyv14tgMMgrA9asWYOKigrW8powYQJeeeUVXHPNNWhqasJpp53G1R4AXHLJJdi0aRPuuecefPrppzzDMJlMjEYTgRsFBQUcvNVqNWpqajjLJ84LzefI+QO9lZW4UEwul+P7779n59vd3Y3ly5fj97//PSKRCNxuNwcZei1RQFRUCnC73Tz/icVi3LYVh/VEoKQkSES60b2nzyW1TKnqp0Arwo5pmE/tQo1GgyFDhvD31OPx4Pjjj09TOXc4HGhsbPyxbuZ/zv7ng0symcTll1+OdevW4euvv0ZRURFSqRRaWlqwfv16vPHGGzAajbjzzjths9m4hVNXV4dnnnkGBQUFGDNmDE466SRuF1G7ZtmyZfj0008xZswYPPHEE1CpVCxlQmV/X9Z4IpFAbW0tw4sXLlzIpTnxYx555BEsXryY0VI0rLXb7dyqkEgkWL16Nb799lssWLCAnQm9h0icpCEoocaIK7B48WJcffXVGDx4MM8BJBIJ5syZw06NhrJUBZlMJnR2duKBBx6Ax+PB7t27MWrUKHYKFosFzc3N+OKLL1BVVYW9e/di4MCB0Ov1uOSSSxjSLQZdo9GYNrBvbW3Fnj178PLLL0Mul+Puu+/GCSecwJInxJ5uaGhAZ2cni0FGo1F88cUXOHjwIC6++GJmldMMKhAIsO6Z1WqFRqNhfpHNZoNEIsH48ePh9XrhdDr5OHt6enhWIprZbIbVamXNtwkTJmDmzJmQSCR45pln8PTTTyMej6OhoQENDQ3H/Hw6HA5UVFQw+i0ejyM/Px+PP/447rvvPoTDYcydOxeBQAAmkwlSqRSPPvoozj77bLz66qu44447+LUUCgVGjRqFnp4etLS0MHkR6GWjJ5NJ+P1+BiGI64qpGvX5fKwY4HQ6mfD5zTffcNVCtnv3bq4SSaKG0HkU9ChgUUBIJpO8ZK29vT1tFkOfXZoHEdyakiT6fhDIAgB/JsQFXyQKSkkILZsjciRxrcR5lNvtxsaNG3HppZfy7+g8MnZs+58OLqlUCr/73e/w2WefYePGjSxn7vf7sXv3btx0000IBoO4//77MWbMGP7S1dbW4o9//COCwSDmzJmDUaNGpWVkPp8Pf/rTn1BVVcUwUrfbnUZkpC+ySOCjoLZ9+3Y8//zzuPvuu1FeXo5kMsnEvKuvvhq/+tWvMGbMGABHFpKRvAoAbgE8/fTTuPrqq7ndIg5QxUyPIMek7aTVavHMM8/guOOOw0knncQS+SQUSNllMBiEwWDgLFCn06GnpwcPPPAA3G43kskkPv74Y14Y1tTUhG+//RYOhwNDhgzBsGHDMGfOHAYIUGtOVBoAwO20VCqF7u5uvPHGG1i3bh3OPPNMXHjhhax7VVhYyByGQCDAnAu5XI7m5mZ88sknmDBhAi6++GJ2KuTYfD4fOjs7eZ5ASDVRwZlaQ8TYJsvJyUFnZ2daQNy+fTtee+01PPDAAzjuuOOQnZ2d9tm7//77MX/+fNx777346KOPjvn5HDx4MLdwpNLe1cw1NTWIxWIYOHAg5s+fj2effRZlZWUwmUzQaDTMWH/11VeP2XKl+RTNnKgiEDXPRCIqnTdBzgOBAMrKylhcsrS0lO/PCSecAL1ej8OHD/NMqb29Hbm5udBoNMwZoURGlNQnSDAFM/pMAEfIk1qtlpUmaC2DWLVRQHE6nSzdQlBsiUTCqg6tra38naXgRkGSZj59JYCOZSK/J2NH2/90cHnyySexePFirF69mp010Cubf8011yAQCOC2225Dv379OANuaWnBokWLEAqFcPXVV2PChAn8QY5Go+jo6MBLL72Enp4eXHbZZTjvvPPQ2NjIvA1iShNc8vDhwygqKoLFYkEy2bte9a677sJ5552HyZMn8zFFo1GsWLEC8XgcF154IQsB9g1QQO+X7NVXX2WkGmXmxIRWKpVpq41Fs9lsWL16NXp6erB48WKkUilWY6ZhqUhAozW7Wq0WHR0d+O1vf8uBBQC2bduGPXv2oKSkBIMHD8a8efNQXl7Om/yooqDKipaAUbuOjo+qDlJdvv/++2Gz2Vj0UKPR8DCcUGe09Gvr1q349NNPccMNN6C8vJwdEmWuXV1dcDgcaTIp1DZ0u93MHaJKsa/RwJ+2YqrValx++eXYunUr1q9fj5kzZ+Kuu+5i0iptB5XL5XjssccwevRo3HfffUe9rs/nw4oVK3D22WdDJpPBbDaz3lY8Hsfpp5+O9vZ2PPHEEzAYDBg/fjzy8vJgtVoxd+7co16PRD57eno4o6c2Jjlv+kyIkF5xWRfNSUj9gSqHU089FUVFRdBoNHj55Zdx9tlnp6kyi3B7tVrNg3UxgSAtOFoARsGSqmZqc9H2TdpjQ0GfduKQgkMgEEBRURHUajVMJhO+/PJLAIDdbmceEFUeVPl1dHSgsbERhw4d4nmXGJhEIxRcxo5t/7PB5c0338Sdd96Jt99+m7NDoDfrueiii2C323HxxRdj0qRJLK9eW1uLl19+GeFwGFdddRUmTpzI7OJEIoGamhq8/PLLAIAbb7wRw4YNQ3d3N7eNaAhKX276QpN8t8/nw8MPP4y8vDycccYZvFgJANatW8dtMoPBgFAoBK/Xy31ikSm8c+dOzpxpCE0lPjlyQj+Rc5DL5awvtXz5crz66qsM7SVwAe1Np8xXJpNxtvvFF19g8eLFRw045XI55s2bh6FDh3LQIE4HORbiepBUPP0fVRaNjY3YvHkzDh48iEsvvRTDhw9n1QN6vEiGFJdNvfjii/B4PHjssce4DQiAAwU5DzGQAUhzppFIBFarNa210tfkcjlDjZPJJC677DK0traitbUV69atw7p16zBy5EhcccUVGDFiBL+eVCrF+eefj5aWFrzyyiv8ehqNBr/61a/w+9//Hvfeey8eeughnHvuuSzjHwgEEI1GceWVV6KrqwuPPvooFi1ahH79+h21UiAQCPCcsKurixfTiQi4aDTKWTsBJQjOS1U5Ifuo/etyudDZ2ckor0AggK6urjQypNFohMfjgdvtRm5ubhrBVavV8joHEsmkGYoILKCKQi6XMwSd+Fz0eSI5/7y8POzZswcAsHz5crz11ltMun333XcB9C7Y6zvrJN06asdJJL3bYAHw8jQAacGEmP0ZO7b9OP2L/zJ78803sWTJEjz11FNYsWIFcnNz8cEHH6CzsxNnnnkmDh8+jNNPPx3nnnsuS1zU1dVh5cqVCIfD+NWvfoXx48czCkqr1eLbb7/F888/D4PBgNtuuw39+/dP2/UN9AYuclbk5AOBAFpbW9HU1IRHH30UX3zxBa644gpuARCbfOHChZg2bRoGDhzIWSMAVk+mfweDQaxYsQK/+c1vkJOTw184QvVQkKHsMTs7G7m5uRykqJU2dOhQAL1tFKvVyhBmIqdJpVLU1tZi+fLluOGGG7Bo0SLOEIn0RsFh165dzOQmWRdarEXZH8FwS0pKUFhYiOLiYlYyeOqpp5CTk4MHH3wQw4cPZ+dHvAkaUIus766uLlxzzTWorKzEo48+ygz+nJwcDBw4EMOGDTuKRCgKL1JWLpf3LmYrLS1Fc3MzOjo6jgnCEE0qleLaa6/F9u3bcccdd7Ak/J49e/Dqq69i0aJFaGpqSgvEt912G0477TT+96WXXoqFCxfi4MGDuPzyy3H99ddj+PDhWLt2LatL07HefvvtKCoqwuLFi+FwONIcHjHZSXyTMna73Y7c3FxGZpGSdVZWFs/RRNY8tReJk0KrIKiN5vP5YLfb06pWmrXQcD0SiaCtrQ0OhwNut5u5PbFYjBGExB2j/yOyLM2+KDEBwPeLji0/Px8bN25kouVXX32Fjo4OFBQUYOfOnTx8dzqdjGYT7xl93t1uN5ODiRBLMzFxoJ9pi/19+5+rXNavX4+rr74aZ5xxBq655hrceeediMViOP/885GdnQ2z2YypU6fizjvvRGNjIwKBANra2vDSSy8hGo1i/vz5GDt2LFQqFUpKShCNRvHqq6/igw8+wOzZs3HKKafwF4oWYMnl8rRFSlQNUC/b4/GgqqoKn332Gc477zwWayR01BNPPIHy8nLceuutiEQicDgcPCehGUh2djaSySSWLl0KvV6P448/HpFIhL9olFlnZ2czcktcpqRSqfDJJ58gLy8Pc+fOTePe0LFSMG1ubsarr76KeDyOUaNG4cEHH8SwYcMgl8vZebS2tvLfiVshWiqVgsvl4tkIcTVycnJgt9uxePFifPDBBzj55JPx17/+lZefUZul72sR+Q3oDbgLFy7EggULMGzYMADgAF9eXs671ymAU/uHnGkymcSePXtQX18Ph8MBr9eLpqYmjB07Fps2bYJarUZRURHKysowbdo0tLa24sorr8SkSZPSZmo+nw8nn3wyhg8fjnXr1mHDhg0Mtf7b3/6Gq666CgsWLOAk5Pnnn4fH48H27dtx8803Myz61FNPRWVlJT788ENeVHfRRRdh2LBhHMgfe+wxXH/99XjqqaewZMkSHtRTixA4IvNCn79oNMqbQMXKjf5Osxeaz1GLlBImmkvRZ5qCjsfjgdPp5O8BPY8EX6nKEKHo/x973x0edZ11f2YmfSaZkknvBZJAAoSS0HtHQEFEREAUFUFFFLusBey9rQ0RBRGpobdILwFCCC2B9J5JmZZMSSaZmd8fee7lOwT33ff3rruuep+HZ9e0ad/v59577jnnkmi2sbERZrMZCoWCoS56flRE0BbWmx0CzGYzVq1a5WKY+cwzz2D37t149tln+bWLRCJs2bIFzzzzDMsFJBIJbzq1WCwM+QrvAalUylY9IpEIXbp04ST/V3SOP1VyuXbtGqZNm4ZevXph3bp1yMrKcmF7NDQ0oKmpCS+//DL0ej2sVivq6+vx6aefwmKxYOrUqWxaR1DY+++/jwMHDmDcuHGYPXs2zGazi3Fke3s79Ho9D5qBGxYUBDFotVp88skniIuLw+TJk9He3s4V0p49e1BYWIht27ahpaUFxcXFrHYm/Jkgj6amJhw6dAjffPMNPx5VgFQlS6VS9O/fH+Xl5YxNSyQSaDQaZGRkYNOmTZBIJDAajQwHEDZtMpmwb98+/PLLL5gwYQImT57MFa9KpeKhNrGtfHx82Ovr5hAmV9Ih2O12bNu2DW+//TZSUlKwdetWxMbG8vM3m828KVOo+VGpVGzuWFhYiCeffBLvvvuuixuxWNxhGU/Qls1m44PO09OTYR0iAZSUlECn06F79+5ITU1FbGwsGhoa0Lt3b1RWVqKqqgp5eXmor6/HpUuXsGrVKiQmJuL+++/HnDlzmPpLkOSYMWMwfPhwnDx5Elu2bEFjYyM+/PBDbNu2DR988AEn9CNHjsBgMECtVqOmpgbl5eXc8Y4fPx6jRo3CmjVrsHz5crz++uvo1asXgA7o5oEHHsDLL7+MuXPnYty4cQBuQIT0Hgj9uqgjoKVrzc3NcDqdCA0NhUwmQ3V1Nc9kaEYkTDQ0aCcrGCHVnRh0DoeDab7kvq1UKnnYTz9fW1vLli8Gg4GLAeqoSeRJvmX0/gqvp6ysLJfZiN1ux6FDh7Bs2TJ2Kgc6kmV5eTkKCwsRHx/Pi9uIjEMzRWEoFAqYzWZmiBGkTVs5/4rO8adJLg0NDZg4cSL8/PywY8cO+Pj44OjRo51+rrW1Fe+88w6WLVsGrVaLTz/9FE1NTZg4cSIGDRrEVEeTyYRHH30U165dw8KFC3HXXXehuLiYsWrhsLq5uZkxY6qMharkzZs3w2g0YvHixXB3d+fBf2VlJdasWYMvvvgCcrkchYWF7A9FMBOxtEwmE959911MnjwZwcHBbG5IlTRRbT08POBwOJCeno7S0lKUlJTAZrPhnXfewX333cezIUq0QIclSWlpKd5//30AwBNPPMFmfkIFvsFggMFg4IT9a/CREGohBfeePXuwceNGxMTE4LvvvkPPnj35ZpZKpYiOjkZRURG2bNmCCRMmwMfHh5MLOTPn5+fjsccew4cffsi2/+SaTAcdJUnqmui9IXiwqakJEokEr776KsLDwxEQEMAD/uLiYqSnp7u8li1btsDhcODKlSu4du0annnmGTz//PO47777EBkZieTkZPYSa2trQ9++fdGrVy8cO3YMO3bsQFlZGWbOnIk1a9ZgxIgRLDLVarU4f/48J1N3d3emii9cuBBWqxUvvfQSPvroI0RFRaG9vR09evRA//79sWDBApw5cwahoaFcgFDI5XImjtCyK4VCwew3sViMoUOHwmazwWAwMN33Zl2WsLKnQoaG70K/OEo8JC4lDYvJZOLkINRbeXl5sRURGUpSd0XsNSratFotAPAMcNOmTVyskLi1tbUVX375JYAbbt8U+/btw8KFC/m5AjeseYRsQHq91D2RfY6/vz+7PvwVneNPkVxsNhumTJkCrVaLkydPsur2VskF6Bj4nThxAjt37oRWq8XIkSMxduxYvkm1Wi2++eYbaLVaPP7445g4cSIfqgSxkAKelOpC2isN0dvb21FcXIyjR49i+vTpUCqVvJ9CJBJh/fr1GDFiBObPn4/Dhw+7iB+pKiaIIysrC3V1dRg0aBDq6+shFt9Y1NTW1sYVbHNzM4qLi1FaWgq73Q5fX198++23CAoKwoQJE/jg9/PzY0fb3bt34+9//zvuvPNODBs2jLszUkNLpVLk5OSgpaWFnZaFAjVhCCnbDocDp06dQmZmJlQqFebPn4++fftCKpWiqKgIXl5e8PLygkqlQn5+Pu655x4MGTIEra2tEIvFUKvViIuLg1KpxKVLl7BkyRI899xzaGhoQENDAwsbSelOw2CdTseL2mgVATGgkpOTERER0YlFR5DczTF9+nRMnz4dzc3NWLduHdatWwetVov8/Hx8++23iIiIwJQpU5jQYDab4ebmhnHjxmHq1KnYsmUL9Ho95s2bh0mTJuHJJ59kPZEQciJ9Fa0qeP311/HCCy9g6dKlbEsEAI8//jgefvhhPPvss/joo48gl8s7+ZjRXCUyMhLFxcVs9wKAB+hk6U9kFgBMMCHau9CvjIb31M0INSzkhu10duyAIfIG/S4Jd+k5tLa2orW1lfVJFJR46uvrceXKFfbgk8lkcHd3x9y5c1FbWwubzYZvv/0WixYtgtVqxZUrVxgCzMvLQ0JCAhwOB1vfkIiUXL0lEgmvqBa+b9RNUaJVq9V/JZd/EH+K5PLiiy/C09MTmzdvRnJyMoCOG+XkyZOdflYul2PYsGHYt28fRCIRhg0bhmnTpvHhXFRUhDVr1iAmJgbz589H165dUVJSAj8/P76ZaLkQWWhIJBKuhIxGI1dt5FOVnp6OIUOGAAAnj4MHD+LSpUs4cOAAdxCEg9MBQNiyXq/HiRMnMGHCBJjNZrS2tvJuGDJ5pPlPS0uLiylkcXExjh07hrfeeosxcqqUFQoF1q5di8zMTLzyyisIDw+HXq93GdjabDaUlpbCw8MDSqWSTSJvhhbod6jTyc3N5Zt++vTp6NKlC9zc3GAwGPjADwoKgqenJ1atWoXdu3fjjTfeYMo4dS1eXl64dOkSXn75Zbz88ssuCYDmOjqdDiEhIVxht7W1QavVIiAgAJ6enpwsJRIJ6zP+t9GzZ0/07dsXb7/9Nn766Se8+OKLAIDKykp8/vnniIiIwPjx45GYmAiZTAa1Wg0vLy/cfffd8Pf3x2OPPYa33noL48ePx6xZszBt2jSX/fFOp5NdAYia+8Ybb2D58uX44Ycf8OKLLzL7bPHixXjnnXcwZcoUTJs2DSqVqtPsRa1Ww8PDA3l5eS4HOJk7lpaW8sCeqnoytSQlP7G/iL5Mhy91v0IHZipGiBZvNBrR0tLCnxf9Ln2uQtdrKkhokZiPjw8OHToEoKP7mD9/Ptzc3BAfH4/g4GDuaEgjlpiYCB8fH5SUlCAvLw/z589HQEAAk0uoUyJ0gRIguRAAgL+/P1O1CW79K7n84/jDJ5fNmzfjvffew3fffYcxY8bw1xcvXuwyGE5ISMDtt9+O1NRUPPvss6ioqEBycjIefPBBxp3PnDmD9evXw83NDcOGDUNISAiampp4sEltODFziJdPuy1MJhNXX2KxGEeOHEFjYyMeeOABPtxI+LVr1y7Mnj0bCQkJaGho4BuGWnPqXpxOJ/bv3w+JRIKuXbvyTUwVpxAjp46CoCK73Y4PPvgA48aNc3FmJvz8k08+wcWLF7FkyRIoFAo0NzdzNS3cjUH6FLFYzEp1opLScFQkEqGpqQlHjhxBVlYWwsPDMXz4cF7mRTAfYfROZ4el+vfffw+pVIpXX30VvXr1gt1u54TpdHasOVizZg1WrFgBrVbbSbtDBxgNiYXCUaKeCjvBmpoaXgImDKGH1s3h5eXFP+/m5oY+ffrg008/xZ49e7BlyxYYDAamGo8ePRqzZ89mijh9XgkJCVi9ejUOHDiAt99+G7t378aCBQuQkpICkUjE3QdBrkSP/vjjj1FdXQ2RSMR7e9LT0zFlyhTs3r0b48aNY6KKxWKB1WrlORqZiNK1R2GxWHhjo5AAUF9fj8TERFgsFmg0GiQmJrIzA72PJKwVDs8pOQA3BJF0LdNnQdcPuYTTPUTvt/DvFRUV8aF+8uRJ3H777SwCJeV9ly5dmKJM15WPjw+6dOnCLtuUwMiKhrpDq9UKpVKJqKgopunT/S38/P39/fm+/Cs6xx86uVy7dg3z58/HQw89hPvuu4+/vmPHDqxevRpAR3X85ptvIiQkBEajES+99BLKy8sRExODZ555hofnp0+fxk8//cR7XGJiYjg50cFNDBrqcggnJmiKDhNyFN65cyfGjBnDw0m6cPft2wen04lZs2ahvr4eDQ0NLhi28KasqqpCbm4uHnroIQCujrHUOVE1SBWqTqeDt7c3jh8/DgC4/fbbGUt2ODo2A3766acoLS3FsmXLeOmS0JeMDlqaMRHdlFYFSKVSqFQqtlTZu3cvLl++jN69e2PJkiUsBhS6BZABYUtLC65fv44TJ05g0KBBGDBgACdMMt4EgPLycnz11Vd45ZVXeGcNGRuS7oV+VlghUydIBAEKsrOprKxETEwMRCIRjEYjrly5And3d0RGRrJok0IsFrtAKJWVlWhoaIBYLMa4ceMwbNgw7N+/HxkZGbBYLLh06RJOnz6NBx98EGPGjHFhaYlEIowaNQpdu3bFli1bsHbtWoSFhXFlLiSK6PV61otQoqRwOByYPn06HnnkEbz00kvspeXh4YG6ujo4nU5W21dUVKCxsZGhscceewxWqxXl5eXQarUuBzB1T6Q3oeRDcxzSQ6nVahdnYbpGiDZOnRtppGjQ73A4eM03+cEBN3zH6P3eunUrd+0OhwPr1q3DzJkzOUlSAqL7gAoyu92O4uJiZvIB4O6I5jjUXSkUCiQkJECr1bJJrbe3NxobG1FZWYmwsDCo1Wr4+/vztftXuMYfNrmYTCZMmzYNiYmJ+Pjjj/nrNTU1mD9/PgAgLCwMBw8ehFgshkajwVNPPYWioiKEh4fj1VdfhVKphF6vR0ZGBjIyMiCXy3H//fcjJCSEbzahChkAiwsB8GpUs9nMhy7BGlu2bIFCocDAgQMZspJIJNDpdMjIyMDMmTPhcDhQUVEBo9EIm83GnQpwQx28e/dupKWlsTqdgi52Ui2TCps6hOrqamzcuBErVqxgDYLdbofFYsGGDRtQU1ODl156iWE6ggsosQlvdkoSxOCi16nVarFv3z4UFBRgwoQJePjhh7mS1el0/JjEPKIKcefOnbh69SoWLlyIhIQETnzEYAM6DvGvvvoKjzzyCEOQdADbbDYW2RGsQtUyeVwRfEhwDM0MGhoasH79epSVlSEvLw/l5eXo27cvsrOzIZFIkJCQgAEDBiAhIQEzZsxgyxkAPM+h64BYSFOnTsWIESNw6NAhTjIfffQRDh8+jEWLFvEMEACLWe+9915069YNzz33HDQaDZ555hmGWympkFbkVsJVlUqFESNGYPXq1XjjjTcAwGUrpEQiQWhoKFf2Op2OrXxKSkpQX1/v0tkTsw+4kajJzbqtrY0dEi5cuMBkDCJrCHcWEcVeqKOh98/T0xMymYzdjKnDoHA4HNBoNCgpKeGv2e125ObmYtSoUVAqlS70ZLoPaH5FnQxpwuh9o4RCHb3ZbGaY22g0skN6SkoKd+OkE5LL5S7X0V9xI/6QIkqn04kHHniAnVnpgzebzZg8eTJ0Oh2Sk5Oxa9cuxnfffPNN5OXlwd/fH88//zw74X766afIy8tDaGgonnzyScbkqb2nmQoAHlSSs61Go2G6L/kbicVi5OfnIzc3F3feeScnJ7oJduzYAU9PTwwZMgR6vR5NTU3MvRcOOMViMc6fPw+TyYThw4fz66bv0QFP/wAwhk5q5f79+8PX1xeVlZVczW/duhWFhYV45JFH0N7ejqCgIPj4+MDT0xP+/v68o4UMIqlipSTU0tICrVaL9evX491330VKSgrWrFmDu+++G2q1GgqFAu3t7Qx70HNta2uDTqfD119/Db1ejyeffJLpxfR4dNBUV1fjiy++wNKlS1mgSOJO4WslNbhUKuUDAuiglZJCneYXHh4e+OSTTzB79mycOnUKgYGBeO6553Dy5Em8+OKLeP755zFmzBhotVpkZ2fjmWeeQZ8+ffDKK6+gpqbGBWYDwAw1SsZSqRSTJ0/GN998w7Y+Fy9exOOPP44NGza4HIgqlQpqtRojRozAu+++C71ejxdeeAG1tbXcRZpMJhgMBri7u8Pf35+9wsiDTiwWY/78+fjqq6+4sBHqVug6oeKA4CRi0gl9vigJCH+XfPjos6QCoLW1FcHBwVAoFNyVEoOLPnuiQ9PnRgUXdde0PptWANB9JhKJsHfvXrS3t3OxQDTlgwcPusC/xFij65SgZIlEAplM5qKfETLIaG+Mp6cnKisrXT4Xm83GlOampiY4nU7Ex8d3WhT3V3TEH7Jz+fLLL6HRaPDjjz/ywiiHw4G//e1vyMnJQUxMDNauXcuVUE1NDUaMGIGKigrce++9kEqlMBgM+PTTT5GTkwO5XI6VK1cyrZNmJj4+PuwVFhISwitwSQOzcuVK9O3bF3feeSfPIsRiMQ4fPozBgwcztEYzCbPZjJqaGowbN459rQhSIzyY5jhWqxUHDx7E5MmT+UCjapAYacIDVaPRQKlUws3NjRXzEydO5Bvbbrfj+PHjOHPmDFauXAm5XM67NEggRzCbcCWtcNmSyWTCmTNncPnyZYwaNQoPPPAACyN1Oh3DVESJptckkUhQUFCA7du3Y+LEiejbty+zjaxWK2QyGVOfq6qq8OOPP+KZZ56BTCbjxO7u7g65XM4aHaqmw8LCIJPJoNfrXbqtmJgYtiVZu3Yt1q9fjzvuuANbtmyBr68vAgMD2c4FgItX14ULF/DJJ5/gxx9/xBtvvIF3330XY8aMQWxsLObOncudEXnIUXfm7u4OnU6HJ554AgMGDMC3334Lo9GIffv2QaVS4b777mNihMlkQm1tLfr27Yt3330Xr776Kn766SdMmTKFtVb0/ojFYsTFxbH+hypytVqNfv364f3338fdd9/NQ3piMdIcTKPRQC6Xs66J4CEh5Gk0GpmUIhKJoFAo+LOnAomek3A1NR3ira2tbNdCM0r6feos6bOjwo3IIQTXWq1WDBo0CElJSfDw8EBGRgYGDRoElUqFoKAgXn9NQYmHkAHhbIUsmYT3DN23Xbt2RWhoKGpqapiFB3Rs6szNzeXzxOl0IiEhAdevX2ei0F9xI/5wncu1a9fw1FNPoX///iwkA4C3334bH3zwAQYOHIjdu3ezsy9RImUyGRYuXMi+TZRYfH198dRTT0EqlbKQjKxNxGIxLzWKiopyqRpXrlwJqVSKcePGsT24r68v1q5di/LyckydOhXAjQGtRCJBVlYWamtrkZ6ezlUgLdGiQ5Tgp4yMDCQmJrJNC/0tqtwpmRFT54MPPsDbb7+N69evY8eOHRgwYABX+hKJBHl5efjuu++wfPly3qVBNi30uBTCQ4Iqc61Wi2+//RZOpxNLly5Feno6dw90YNHQl2ZS1PWdOnUKZ86c4TkE6UvIeyooKAihoaHw9vbGF198gSeeeAKxsbEICgri1+vt7Y0PP/yQdS+pqakYMGAAoqKi4O/vj9jYWISFhSE0NBSxsbFQKpXYvXs3Jk2ahKqqKqxduxaPP/44Lze7eTeLMFJTU/Hdd9+hvLwcL730Enr16oU9e/bgs88+w9ixY7F7927uhtzc3CCTyRAcHMxDZS8vL4wePRqrV6/G6NGjodVq8dprr2H37t0ICwvjnTe0fCw0NBSLFy/G9evX8cUXX7C+RAgfUbEjnI8AwLhx4/D3v/8dRUVF/Jk2Nzejrq4OhYWFsNls0Gg0qKqqwuXLl1FbW8t/m64nsViMqqoqREREdKr0Kfn4+PigpqYGarWadwoRMUCn00Gj0aC+vp4V/mQfQ8mFjF8JYiouLoaXlxez2qjL7dKlC4YMGYIBAwYgICAASUlJ6Nu3L8LDw/laFj5vWvpFMxXyIqNrkGYp3t7e/HNlZWW8WVQYAQEBaGhoAACepep0OtZ//RWu8YdKLjabjRlWK1as4K8fP34cy5cvBwA8+OCDiI6O5vaXdm0Qg8ZoNOKrr77ixPLCCy+gZ8+ekEqlkEqlUCqVTFmUy+Xo1q0bxowZg8jISKhUKohEIqxYsQJSqRRPPPEEV3B2ux2XLl3Ctm3bmH0lFFrabDacPn0agwYNYk2MUGxJ3Q25+Obm5qJHjx58sBCWTNUY4f1+fn7M8tLpdPjqq6/g6+uL3r17MzRVWlqKr776Cvfddx98fX1hNpt5RYCwEqXqlqwygI6kU1ZWhlWrVqFfv34YO3Ysd1u04KqiooJvVFLjk2YjMzMT2dnZmD17NqKjoxEdHY2goCCo1WoEBwdDrVbzTGzlypVYtWoVYmNj4e3tzZoLqVSKn376CUOGDOGEQ+JHCmKykWHl0qVLkZ+fj88++wwPP/wwP0Z7ezsf8P9ThISEYMWKFXjyySe5qjcYDFixYgXuuece5OXlISgoiAf+JEqkosHHxwfPP/88Hn74YQDAY489hpdeeolhLDrE/fz80LVrV8ycORM6nQ5r1qxh63qyQ9Hr9airq+OuRSzu2PwZHh6OsLAwnD17lrsFm83GtvQ+Pj4Mg2m1WvYho8el97mlpQUqlYptcuggJ00U0ZpjY2NZgEnVfVNTE6xWK7RaLYxGI3ve0fyLBLHk50aQmlarRUVFRScNmdCZgWaRxIyke4QSCwAmDJCuixIbJRvh71gsFr6PZTIZ2+gAN6jHTqeTOxqtVouzZ8+6OAD8FR3xh0ouL7/8MvLy8rB+/XrGZBsbGzFr1izY7XbcddddGDhwIK5du4a6ujpoNBq+2cmOPiMjA5cuXYJUKsXixYsRHx/PF627uzsCAwMRHByMwMBAhISEICEhgQ/LxsZGvPjii5DL5Xjqqafg5eXFcw7yuxoxYgS74hJkIBKJcPnyZZhMJowcOdJl4ZiQGUaRmZmJ6OhoJCUl8TyC2nzqVujgJWGY8G80Njbis88+g9FoRF1dHbZu3YqxY8ciPj6eTQtpEOvj4wOZTMZqb7FYzHRWu92OixcvYu3atZg0aRIGDhzognsTg8xoNKK5uZk9qegQWb9+PYqKirBy5UqEhYVx8lar1ZDL5bz3w2QyYeXKlVi7di26d++O6OhoqNVqqFQqJCQkICsrCxKJBAMHDgQAVuffKoxGI2bMmIEuXbrgscceQ7du3RAcHAylUsnrf/+3w9m7774bRUVFePTRRzkplZSU4KmnnsLmzZuhUChgtVo77e4hvdO0adPw7rvvAgBef/11PPHEE6ipqUFVVRUaGxvh5eUFhUKB6dOnIzU1FUePHsULL7zAQlzy6rLZbCx09Pb2ZoioW7duOHv2rEtCoEPZz88PGo3GZcZChZfZbOa/T4vFfH19uSujxE7WKU1NTdylE9wqvA6I0k/sLCqKSF9FXYTZbOZ7QOguQDtcqCOktdv0nAl+JEiNNDvUdRMcCNwgpBCNnD4bWiEhEnWsmQ4NDYVSqWQXB1rdTUXKsWPHYLfbMXXq1L8ckm+KP0xyOXr0KN5++2289957SEpKAtBRVc+dOxfV1dWIj4/HkiVLWPeg1Wp53kEH8oYNG5CdnY2YmBg8+uijSExMhEqlgtVqdTncqWuRy+Wor69HaWkpTp8+jTfffBPe3t54+eWXIZfL0dLSgpaWFthsNmzevBl2ux1jx46F0WjkqoiqppMnTyIlJYUH/yTYoptOLBbD3d0dlZWVuHbtGiZMmOCi2aAkRANLGiZ7e3uz6pjC6exwef7+++/x3XffoaWlBQMHDmQaMFlsUGUJdAg1SZBIr+vQoUPYunUr7r33XjaIFFbcQlFma2sra15kMhlWr16NxsZG/O1vf+MqPCQkhJX0MpkMAQEB0Ov1+PDDD/HDDz8gODgYVquVf6axsRFHjx7F1atXce+99/Jelvr6epSUlKC6utoFzisqKsL06dPx7LPPumieaD5F6nehYeE/G2q1Gp9++ikuX77M7sY9evTA8uXLMXXqVDQ1NfEOG+HnAAChoaFYtmwZC2nXrVuHBx54ANu3b+fX0d7eDl9fXxZnXrhwAU899RTPsuh9JUjTZDLxsL179+7Izs7m7lHILvP29ubOgGAzobAQACv2IyIi4OfnB7VaDaVSyQUArYgODQ2FSqVycYUQesDRvUDDcKIIk9iSCBY2mw3+/v4MQdH8hz4rAFyo2Gw2fi4EVVOiIH0OdTtEUyfiA3VdCoUC3t7e3MXJ5XJERUUhMDCQqebx8fHo378/0tPTmYxQWFjIsHFRURFefvnl//V180eOP0Rysdls+Pzzz3Hbbbdh0aJF/PVvvvkGe/fuhZeXF55//nk+kOrq6lwqIk9PT2RkZODatWuw2+0YNmwYunXrxu060UNJ9a5WqyGTydDc3IzS0lJcunQJ9957L3Q6HV599VVYLBY0NTXxjVVaWopr165h5syZcDo71uLW1NTA4eiwO6+pqUFFRQWGDh3KN6WPjw9CQ0N5KEnznQMHDiAhIQEJCQnM1hLqWKiLoYQoEolQUVHBhywloa5duyI4OBh1dXW49957ERoayhUpLWmi/R402Bd6ZP388884d+4cFi5ciMjISMa5qRKlxyGs22KxwGg0QqvV4rvvvoOPjw9ee+017hri4uLg5eWFhQsX4rbbbkNVVRUqKiqwbNkyfPDBBwgJCUFjYyPDJDU1NcjNzcWaNWvw6KOPory8HI2NjaitrUVjYyM0Gg20Wi3q6uoAdLhhL168GGvWrEHfvn1vKYak+L/YqCclJWH37t3Yv38/029PnTqFmTNn4uzZs0xPp0ON1uoCwJo1a9jduaysDJ999hkefPBBfPDBB7hw4QI8PDyQmprKP3P16lXMmDEDTz/9NKqrq5n+3NDQgOrqatTW1qKhoYE/V7JMAcDXjo+PD4xGI1f+VP1TJ+zj44PKykokJCQwy4pEwbQPprm5Gbt27UJcXBwqKyt51wx1EZSwiVVmtVpdVgb4+fmx5T5BViqVCnV1dZz0hcQR+ox8fX1Zp0LiZODG7JHuHbIkamtrY1iXfoZYkMnJyYiLi4PT6URERASLeylEIhGio6Nx5swZTno//vgjiyjNZjO++OILnDt37v/72vmjxR8iubz77rs4efIkfvjhB65wcnJy8OijjyI5ORkPP/ww1Go1H4wajYZpvi0tLTh48CBbwUybNg2jR4/muQod2IQ7a7VaXL9+HQUFBaiqqsLp06cxZ84cREZGcudC8BSZ+33//fdIS0tDeHg4P2fhTXbx4kWkpaUhISGBB6akdA4JCeGb+Nq1aygtLcUdd9zB+8gJZvLx8eGZCP0Nq9UKvV6PK1eu8E0ZFRWFZcuWYfz48cjKysK8efMQEhLiIk6kjon+PwnpqIvauXMnzp8/j9tuu40tWuhxKckRfEG4t81mg16vZ+ru4sWLeU4ll8vh5+cHpVKJkpIS1NTU4Pbbb8fDDz+MH374AUlJSfzeAx2VN9mqPPjgg6irq+Nugxh1pN1obGzErl27sHfvXmzbtg3h4eEuKvFbhdBN+f83xo4di7179+KDDz6Au7s7DAYDXnjhBXzxxRfspSWVShEZGQlPT084HA5O9MJobW3FsWPH8Nxzz2Hx4sWw2+2dzDN37tyJ8ePHY9WqVdDpdDCZTDCZTOxiYLfbkZqaitzcXDQ1NfG1A4BNJKkYoPuHPn/SXoWEhACAC1RF5JLGxkZmc1mtVobT6EAnurNQI0XfJyhOJBKhsbERJpMJra2tCA0NhVarZciPumDqTGh+RjAufZ3uP6IiUyKh2aFQC9Xa2oq6ujruXBUKBdzc3BAZGXnL4sPT0xNqtRrV1dUAgPXr17v8nE6nw+TJk10K1z9z/NdTkQsKCrBixQps2LCBKzrau0Jc+dGjR/NAUavVspBQJBLh6tWr2Lp1KwBgxIgRmDJlCvz9/V0UxZ6enrh48SKWLVuG0NBQPPvss/D29sbly5fx97//HampqXjyySd5qEiMndbWVnz33XeIjo7GwIED+UKkYTdpTk6ePInZs2dzZUVwFLX3crkcbW1trE0ZMWKEC3xEu8SFK4zJTr6kpAQNDQ28erdbt25obm7G22+/jQEDBvDBDaCTyaVYLIZer2fmTHNzMzIyMnDy5EnMnDkT8fHxfMN7e3uz/oBgDJq9eHp6wmKx4Pvvv4fBYMCiRYvQ3NyMmpoahsy8vLxgMBhc1N9tbW24cuUK0tPT0dzczE64ra2t+Pjjj3HnnXcC6IDshMN7OrSam5tx9uxZ5ObmYvny5dBqtTzvIUv7m4MS9r8ixGIxli5div79++Oee+5BWVkZMjIyEBoaioULF8LDwwMBAQEAwJ8ZLTETwrAUNOcSFikUbW1tWL16Nfr06cOUb5ppiEQipKam4ocffsDs2bMRGBjIFjL0XlPQ7wohpOrqaphMJt6ZQlCx1WqFyWTC+fPn0bNnTxfHCqLMU3FGyZzgVvq8KCFQ8qDZXExMDMrLy7nTUCgUaGlpYaYiuYcTTAvcgPuEMxaiF5PTOLHFqBCyWq2oqKiAn58fD/tTU1N/ldARFhaG6upq1NfXo6ysDGq1Gna7HYGBgRgyZAj69+/vYnfzZ47/6uTicDjw0EMPYdKkSS46hNdeew15eXmQyWR48skn2fJDo9HwwedwdFioHz58GEAHvfSBBx7gWYfdbodWq2UX2Y0bN7IN/pdffomkpCRs3boVaWlpeP7556HT6Vy2F7a3t2PHjh0wmUyYM2cOHxZUUQEdN2Fubi58fHyQlJTE1jCEZQPgG/TixYsoKyvD/PnzIRaLkZSUxF5SJpMJVqvVxX+J5ic//fQTL5MipXJFRQUUCgXuuOMO/h264elmpJuLGG0SiQRHjhzBvn37MGPGDHTv3p1/j+YsxKhrampi919Keps3b4ZGo8Hjjz/OQ9vKykr2ODObzTh+/LjL/pHW1lY89NBDkEgkmDhxIs/JyJ4+LCyMdR1C40PqGHNzc5GdnY233nqLmUC1tbUIDw+Hr68vQkND+XAic9GbVwT/K2LAgAE4ffo0lixZgoKCAnzyySdobGzEqlWr+H1ubm6Gm5sb/Pz8EB8fj8LCQpe/ERUVhVdeeYU1KTdHdHQ07r//fv7MqIOka4F0IKTqp27T19fXRcxK/2j2UlxcjNjYWC542tvbeZUxdR+kYhfS1oVEFNLu2O12nlVSIUNducViYVdlut6Eei1KMP7+/mhvb0dBQQFkMhkP+Wk1gUgk4hXYtAqC1osLOxdhd26xWFBSUgKVSoXq6mp4eXlBo9EgMDCwU5Kh5KJWq3Ht2jVEREQgJycH58+fd4Hk/4r/8uSyevVq5ObmIi8vj792/vx5vPXWWwCAZ555hj2U9Ho9dxRABy1048aNaGtrQ/fu3bF8+XKukKuqqrjDaW9vR1FREc6cOcOPQdsEJ0+ejGnTprl4ZAEdUMKFCxdw9uxZPPTQQwwN0cFHCc7DwwNnz57FwIED+WYkURolKvra1q1bERcXh7i4OF7D6uXlhdraWq40ad85ufwePnwYer0ejz76KA9P3d3d0adPH/Tq1YuTIHVpwgMaAM9e9Ho9Dh48iB07drC5J70WoYBOKIYjJpzNZsOePXuQn5+PxYsXQyaTMSxC1S0lqEuXLnHiJQz9rrvuwrBhw3ivR2ZmJjw8PJCUlMQJjCpZSrbEYjt//jxeeuklFyNLmgd4eHgw6+d/8oaqq6vDL7/8wrCh3W5HdnY2evfujRkzZjBz6B9FcHAwfv75Z2RnZ+O2227D+vXr0dbWhrVr18LT0xNOZ8cyrKamJvTq1csluQQGBuKxxx7jQ5OcCyi6dOmC1157jQW+VO3T+0yfh7u7O/Lz89kqn67Dbt26MaxEXb1EIkFzczOys7MxduxYXq0sZJx5eHjgwIEDiImJcREq0v9S8UEQsNCSSNhdCd0XyM6GaPXu7u5QqVTMIlSr1TxzS0lJwfXr19mfTFhYdenSBe3t7QgICIBSqURVVRV8fHyYCQaAr12bzYaGhgbW5fj4+KC+vh42m41F2BRhYWGoqqqCWCzmRXZdunTBhg0b/sdr4M8W/7XJpbq6Gs8++yzefvttvtlsNhvuu+8+2O12DBgwAP369WMsmMSIBFtt3LiR6aGTJk1CXFwcK4mBG+7DNpsNR48evSUGGxgY2GnYKBaLkZOTg/3792PWrFkICQnh4SkFteQ0cE1PT+euwcvLi115yV7/8uXLKC4uZlNBso0nryRSylPl2NLSgvLycmRmZuKOO+5gI0GhqeXNg1uCCoj9Q/oGh8OBo0ePYvXq1RgzZgzS0tJcXgtBYZRULl68iDVr1uDee+9FUlISDh06hIqKCixYsAD+/v6sSRAmJKKSFhYWQiwWQ6FQ4K677sKDDz4IqVQKu90OjUaDS5cu4dKlS5g9ezZTboW7OAjrvnz5Mk6dOoWHHnrIxaWAgjQTFL+WWPR6PTZt2gSlUom77rrLJUnNmzcP58+fx4oVK3iXSEJCwv943fbt2xcnT57EmDFjsGnTJhgMBmzdupWZXnK5HAMGDMCmTZsAgJMgdYBSqRRBQUE8i/D29mZFv1CQ6OPjg+bmZk4gbm4dO+Y1Gg1fj0qlEq2trUhPT3dRytP1QWapQsdjGo5Tp3v9+nXMmDHDxfdO2DFZrVaXa4kozrSrh8S+NOciPRIAxMfHo76+HsHBwcwOo+uXng9BzEJGmXCITxY5AFw0MvSzhGLQzPDKlSvw8vJiZwq1Wu2idQkLC2O0g0KlUrms2f4rOuK/Nrk8+uijSEtLw4MPPshf+/rrryGXyxEUFIS7774bdXV1XBUL4+DBgzyUmzx5MsLDw5kXr9VqodfrYTQaeeB++vTpWz6H7777DnFxcYzRtra24ujRo/j73/+OOXPmYODAgTwHIdEWcGPF75EjRzBw4EDEx8czHBQZGYn+/fuzg2tdXR22b9+OyMhIpKWlwWazsY0IAMarCQ8n80naE0P7T+gxqdKnZECHPSnIfX19+RBVKBTYv38/vvnmGwwaNAgTJ050GcQSbEGQmMPhwNWrV1FXV4f33nsP/fv3R3Z2NubNmwe1Ws3EAAph9bp9+3b+XPv164egoCCGYWj17A8//IBly5axrggA4+RAx7xEo9Hg2LFjuP/+++Hp6QmNRsO7ZYgK/I+G+UDH4bRnzx4YDAbMmzePO09hiEQi9O3bF3379kVeXh4eeeQRjB8/Hk8++WSnRWM3R1xcHE6cOIGxY8fi4MGDmDhxIg4fPswD6vDwcH6vS0pKUFRUhGPHjmHcuHHQaDSwWCx46aWXoFKpcPz4cWzevBlpaWno0aMHH5b0ftjtdu4io6KieJsp0NERkxM0FUfUxZK4NjIy0mWvC10zTqcTR48e7VRs0N+lw7uwsBByuRxDhgyBRqNhhhexEamDVygUMBgMADo6m4CAADgcDhw+fBj9+/fn2Y9UKmX6tFCTI/xchDRqoUml0FFCyDoj0kNOTg5sNhuuXbuG1NRUFn/S/NTd3R0BAQGorKzs9JkKbWf+io74r5w8HT58GBkZGVi6dClfLBqNBi+++CJOnjyJOXPmICAggOcOTU1NPNzLy8tjiKt3797o1asXuwU3NjaipqaGxWAWiwUZGRm/qnsgj6TW1lYoFAqcOHECn3/+OZYsWYJp06YhNDQUfn5+rLAnYaNSqYTNZkNdXR3GjBnD4jFfX1+EhYVxlRYbG4urV6/CaDTijjvuYHKBUDhG+DglmPb2dmzbtg0tLS0YOnSoy/Ol7kjoVUYwipeXFx/mQMfBlJ+fj5dffhl9+vTBwoUL+ecooQE3BrQAuMOgGzcrK4v3nRNjCLhxYxPsV1RUhGvXruH1119Heno6AgICGGpqaWmBt7c33nnnHdx7772Qy+VspEm6CYIvydH56aefZmcCoRVJQ0ODi2DuVkHsvn79+mH27Nm3TCw3R7du3XDw4EG4u7tj9OjRv7rhVBihoaE4duwYBg4ciMWLF0MikSAsLIz1FUuWLMHTTz/Nc5T9+/ezbYrVakVAQAC8vb2RmpoKADhz5oyLJxdZp0RHR7NoNyIiAlVVVfxZ0cFJCZe6ESoeCgsLERsbywc4DfCpwCgoKEBiYqJLJ0BB8GRRUREGDhwIb29vhqvCwsKYju3t7c3dAS3kCggIgLu7O4KDgyGVSl0+A7PZzLtcqBOia0polElBMgIiJ9w8Q6FZU0tLC9auXQuggwVGM6fq6mpUV1czzd3pdLIZ7V/xj+O/rnOx2+1YunQpJk2ahLFjx/LXly1bhqamJqSmpmLYsGFsuS60h2hra8OxY8cAdOxxmTx5Mh+Wly5dclGSO51ObN++HWfPnuXHEIlECA0NRXx8PGJiYpCYmIjg4GBYLBZs3LgRGzduxMsvv4zx48fzoe3n54eysjK0trYyBBUcHIz9+/ejtbUV4eHhXHkHBQXB4XAwPhwaGopt27bBzc0No0aNYnop6U0I5iPWi8PhwIULF3D58mXcf//9vByLcG1yDqY5C812SJ1Oz8/b2xvZ2dl4+OGHkZiYiE8++YTtYOgzoEShVCq5Am5vb+9U1ZlMJnz88cdYtGgRQkNDXZhQIpEIOp0OP//8Mx5//HHExcWxCy6JQP39/bFjxw6ufmn9gHDnud1uR2trK7799lvcc8898PHxYThECIOQF9evRWFhIbKzszFnzpx/yv5FGBKJBEuXLsWoUaNw2223YeLEiXj//fddIBWgg/HV2NgIq9UKiUSCbdu2MYmAVOCRkZEsNiSK+vXr13Hq1CmMGTOG4Vqgo3OLi4tDbm4uswflcjl3k5R0TSYTAgMDUVNTw1RtgsAIEqKigz636upqpkkTrZgIJidOnMDgwYP5/RWSAuizJXiKUAGj0Yjg4GB2HHA6O0wlyTFZaKjZ0NAAp9PZaZWEyWTi7k24yVL4+PQayLxUJBIhLCwMOTk5cHd3h4+PD0Oo1Hnv27ePWXMFBQVoaGhgar8wlEolLBYLzGazy2dL9+I/mt392eK/rnP57rvvcPXqVbz33nv8tSNHjuDHH3+Em5sbZs+ejcbGRuj1esahSbm7efNm6HQ6xMfHY/78+Vw9tbe3w2QyQafTsYhr9+7dOHXqFIAO3HvWrFl4+eWX8eijj+KOO+5ASkoKb6L78MMPsWvXLlZZq9VqdOvWjbuRoKAgBAUFISAgACqVCi0tLdi3bx8mTJgAX19fyGQyhISEwMvLi7ski8WCzMxMHD58GHPmzGHfM4vFwgN84IZmgMSZGRkZGDx4MHr06MHVqFATQ5ASuQxQlUjvQXt7O44dO4Y333wT4eHh+Nvf/oaqqioXSxVi8Pj5+XFHqFQqO23mowMmJiYGISEhLopu6uIOHjyI6dOno1u3bkhISEDXrl2RmJiIiIgI9qlatWoVFixYwMmRKnOyOLFarfjpp58wdOhQBAYGcrKm+RX9k8vlMBgMqK+v77QD5dChQ6ipqcGsWbP+14lFGD169MDFixdhMBjQp08fnD9/nr9Hi8jIBqW9vR16vb4TJZqSDc0R5s6dCwDYu3cvJ0oauhPNuK2tDRcvXoS/vz/PzNzc3LgLJMuU+Ph4FvASHCaVShEQEMAkCqfTiStXrqBnz578WEJLH5PJhPLycnTt2tWFQECwEz3H5uZmREVFcUdE5q3EMKPr2WAwcDcidAUAOlhyZWVlADoSC61HvnnXDDHkqGMih+Tm5maUlZXxAjdyRKbrh/Rrp06d4oRD2jfhRk0K8hSj50RBu5/+ihvxX5Vcmpqa8NJLL2HRokVITEwE0IF1Ll68GECHLbpCoWBjQNK1NDc348SJE8jLy4PNZsPw4cN5yRMxX0hXYbVasWnTJu5woqOj8dxzz6Fbt26sNibs+cKFC3j55Zdx6dIlLFiwAL1790ZTUxO7+RLEIZVKmeni5eWFAwcOwOl0YvDgwXyQEbxHAriqqiq8+uqriI+PR2pqqstGQPp5ggXIU2rt2rVQKBS4//77WVxJBzn9I90JEQEIUiINQUVFBd544w2UlZXh8ccfh0KhgLu7OzQaDaKjoyGXyyGTyXgniru7O3svCYfqIpEIwcHBePjhh7F06VL4+/szYYFYOuvWrYOvry/S0tLQs2dPdO/eHfHx8fDz80NTUxMuXbqEhQsX4v777+fuhGZTwveC1ijQalsKLy8vpuC2trZCo9HwQVZWVsZq/jfeeAPR0dEYNmzYv+Q6VSqV+Omnn/Diiy9i3LhxeOutt5jafqvO6eaNmL6+vkyXFolEGDRoEBISEtDY2Ihjx45xcicyRPfu3SEWi3H69Gk2sqR9NURTJ/iwvLycB/dCH7i2tjb4+vpCrVbDx8cH+fn56Nmzp0uXStfokSNHkJKS0kkZT8mHGI8VFRXo378/ADD7TLjxkYI0LgQ7AeCuIC4uDiUlJXA6nWhubuakTAmMDnuhVZJIJGK7/oaGBhbTSiQSXuZHRYpEIsG3337rAn23t7djz549TF3XaDRsWUOJn9yjKYii/FfciN89LPbZZ5/B29sbc+fOxffff4+QkBAXD59Vq1ahuLgYwcHBmDVrFmpqarhdpha5vr4e27ZtAwD0798fffr0AXDDmoTsUiwWC/bt24dLly5BLBZjypQp6NWrFwC4UDXb2tqwY8cOnDx5ElFRUXj44YcZ0gLAC6w8PDzY8JFcay0WC7Zt24ZBgwbB09OTbTCE+zWsVityc3Nx+vRpLFu2jFkxtE9GSBmlofqxY8fg5uaGxx57DIGBgXA4HGzb7uPjw1UqHWK0+wQAW3lIJBK89957KC8vx/Lly+Hv74+2tjaGF2kHuclk4oEs7fXQ6/X4+uuvAXQcDNOnT0dKSgqLIgmCozh//jwaGxuxYMEC+Pn58V6SpqYmplfv3LkT4eHhSElJAQCGb2QyGR9QGo0G+fn5eOyxx5g1FxgYiNbWVgQEBPAAWq/Xc4VLrzsvLw+vvvoqPv30U6aV/qtCJBJhzpw5GDJkCObMmYPDhw+jW7dueOCBB24587FarS7MNqlUyjvq7XY7Hn74YaxatQrHjh1Deno6rwIWiURQqVRITU1ltbxEIkFycjJKS0uZhk+rsgkiJm2PcABOs76WlhbI5XIXijBBPi0tLSgrK8OECRN4yC+83+i1O51OlJWVYfbs2QgKCkJNTQ2MRiPb7ru5uUGhUCAwMNBlkye9D7SRkrpQSnKUPKRSKR/2QjiK5psEp1Gx0d7eDpVKheLiYqa1k3O2n58fwsLCeCmfp6cnDAYDb9WUSqXMvCM385vXXYeHh0Oj0fC1+lf8zpNLW1sbHn/8cTidTrz11lvQaDR44403+PCuqqrCsmXLoFQqsXDhQqYbU1VEgsWff/4ZNpsNCoUCs2bN4k14RMkkKOzHH39khsyoUaOY4y7EUWtqarB582bU1dVh7NixvKyLWu2QkBAW5gn5/rRf/syZM6iursbjjz/u4gNGbDLCqTdv3oyIiAj07dsXFouFq2/gRtVOnUlDQwN2796N4cOHs70JzReoQquoqEBLSws8PT3ZOp22GNJrXLVqFU6ePIkFCxbwnhiCGMj6Ijw8nN9jgkCcTicqKyuxdetWKJVKPP/88ywmFdJSqVquq6vD3r17sXz5cl4VSxRrgkcMBgPWr1+Pt956i99/tVrNpAV/f39edXzvvffCYDAwy6hLly7cbZGVDs0GiDKan5+Pt99+G++++y4v3/otIjo6Gnv27MHw4cPx0UcfYe/evXj99dc7LZe6lapbIpEgPDwcdXV1SE9PxyuvvMKbSUnsS0LIbt26Ye3ataiuroa/vz+zwFpaWpiFRbMRuubpmqVOgwgfubm5rDQXwkIOhwOnT59mhhitbKCgzxroEIWSw0NDQwOvA6Cft9lsDKGS07CQUEJzEpo/lpeXM1WbDnaCd0noK+xa6efoewDYR42cNCwWCxQKBZ5++mkm9Gzbtg3z589He3s7ZDIZDAYDCz1pfjR8+HD2rKPo3r079u3b9/93kfxB43cNi5E3EtDBKDKZTFi3bh2OHz8OAFi+fDkzZ7p37w6bzcYdBt0YWVlZjI+ScSQtCKOqq6WlBevXr0dpaSkkEglGjhzpIp4i+GH37t1Yt24dnE4nFi9ejNtuu40TGLGmvLy8YDQasX37duzbtw9XrlzB9evX0dTUhKamJuzcuRMjRoxAQEAAezAR9l5fX4+WlhYUFRXBbDbjrrvu4scmrJnYQvR7EokEmzZtgq+vLws6CeMODg5mZo67uzvvSCHvMxrA19XVYceOHfj0008xevRojBw5krVBhG+TbgYAkwGEws+33noLAQEBWLlypYsxoFgshlQqRVhYGO82+e6777BgwQL2cKKDiKBJoMMUcOzYsS5Lu+jwINucgwcPom/fvpws6SAkX7bg4GDIZDJ4enrydkl3d3ecPn0aH374IT766COX/fW/Vfj6+uLMmTNYvnw5ysrKcNddd+Hdd9/lqlwkEjE7zm7v2OdOCdHLy4sXnk2YMAEAcO7cOS5oSMtBBVdhYSH0ej3KysoYFqOfo85FaN5I3Yfdbmcoqrq6Gt27d+d5mvD9v379Orp168bJmu41IoxQJ3358mWGzurr610G6HR9EvW/oaEBGo0GDQ0NaGxs5MOfaORpaWnQaDRsI0QbYAkeI4iYrrf29nZIpVLo9XqewZBmh65nohcTxEuzJi8vL/j6+kKlUnGXK4QFfX19odfrcfnyZZfPWKVSucwb/4r/guRyc5w9exZDhw7Fs88+i++//x4AuAugoDlKQ0MDjh49iqioKPTv3x/x8fFcxdCMoL29HVu3bkVeXh5EIhHuvPNOxMTEALjBmW9tbcW6detw5MgReHp6YunSpejVqxfjzeRVRbBVTU0NXnjhBWzfvh319fWora1FeXk5ioqKcPHiRaSkpLjAYIR5t7S0QK/XY8uWLWhra0NaWhozagjWAm5ABG1tbbhw4QKuXr2KOXPm8I4ZtVrNHQFtJyRqNR1aTqcTOp0Oer0eVVVV+Omnn9CzZ0/MnTuXfbwAsN3HihUrsHTpUk6UwtnBunXrsHfvXjz55JPM+BFqSejm9/DwwIYNG5CSkoL+/fvzayC4jh6zrq4OJ06cwOTJk10+e6FGQafT4fLlyxg5ciQPZqmLamxsRGFhIWpra2EymZjUIJFIsH//fmzcuBGffPIJFArFv8xH7H8KNzc3vPLKK9i/fz/69OmDb7/9FnfffTfy8vIQEhICNzc3NDQ0oLi4GNXV1aisrERpaanL1k7aXnr48GFmNNJnS0LikpIStLS0oLm5mem3lOjpUKWZFx2mRGEmtiERAuh6o3vm/Pnz6N27N/89mnPcPJcRi8UoLS1FREQEU3i1Wi3PBm++jum/W1paUFVVhfPnz+PKlSuoqanhJObh4cEsOHK5IIiaEhoN9QEwlAXcoCkThVrYbdXX13MXZrPZXBT8QAcbMSAgAKGhoQgODkZAQABiY2NRXFzs8vn+xRLrHP91yQXouNF2794Np9OJ4cOHIzY2lnFXYUW2ceNGNDc3w2QyYdKkSdzNCJcI7dq1i0WSM2bMQHJyMldAbm5uqKurw+eff47CwkL07t0bX331FVJSUuDj44Pg4GBERkYiNjaWO5by8nI8/fTTkMvlfDhSNX3w4EG296abgawwqBvQ6XQ4f/48hg8fzkmFEgu9PqGm5eeff0ZaWhrGjRsHhUIBpVLJBwYxwEjZTRsCGxoauDp2OBz44IMPUFVVhfnz5/MNCYAZXd9//z1KS0vx+uuvo729HTU1NSgrK0N7ezuuXbuG1atX46677oJareaOh6A++m+9Xo/du3fj0KFDuOeee3jXBwC2riFSwQcffIClS5e6UD1JeU5D5K+//hrz5893uampetbr9dwVUBgMBuzfvx8//vgj3nrrLX7fhTvSt23b9g+pyv/XEIvFGDZsGH755Re89957UKlUmD17NjIzM2EwGKDT6Vwev62tDVVVVXA4HJDJZEhNTUV4eDhqampQW1sLpVIJAJw81Wo1KioqmEVHe1FooRdBTQRTUuFitVoZutq3bx+Sk5NhMBhcxJUWiwVVVVXo0aMHALhAZdQxUoeg1+s5mROluqWlhSm8NF+jpEDQFqEJJF5ubm5mSI922pPINygoyGXhHlnc0P1PFGohtHcrLY7ZbGYDVYlEgtraWpf9TUSMob9J+rOSkpJOn+9fCcY1ftfJ5eYDAugQrL3zzju4evUqJBIJnnzySQAdK1nJhlsikbisHr377rt5QEi2+3a7HcePH2dR2YQJE9CvXz+ufsRiMS5fvowvv/wSBoMB06dPx3PPPccXNwm/7HY7DAYDDAYDWltbceDAAeTk5GDevHku1bvJZEJWVhYLG0lpTB0UwWoHDhxAcHAwkpKSUFdXh+bmZoYJ6GakGciJEydgNpvxyCOPQC6X8x5woOOgtVgsaGxshNFoRFNTk0vi1ev1sNvt2LFjB65cuYI5c+YAAM9GQkJCIJVKceLECfzyyy+YNm0aunTpwhs8GxsbUV1djfvuuw/l5eUYMWIEmpqaGHqhQ4Ios21tbVizZg0eeughnhf5+PhwoiQ1fVlZGZxOJwYMGMB2/OQe7Ofnh8jISJw8eRL9+/dHXFwcs9fIEJG6HyGdWCQSoaSkBF9//TU+++wz7q7Cw8PZduS99967pRjwtwhfX1889dRT+Pzzz3nD5AcffHDLn7Xb7WhqaoJKpYKnpycXLJmZmTwnoco8MjISlZWV7JdGu4fomibmFFnYSyQS7nKMRiNqa2tRXFyM8PBwprzTIXv+/Hm+9qlDpAJNyBhzOp24evUqkzmoS6YERZ0KLSSjgoEG5hT0uNTRhoWFsV2S0OTTw8OD4WEi5xCaQCQSes50/9AZ0dLSwkw9Sh5CqNrd3R1BQUHcOVF3GBsbi/Lychc69F/ROX7XA/2blfFLlizBG2+8gZkzZyI2NhbJyclMOZRIJFCpVNDpdLDb7di1axeADnry2LFjeZUr3RjXrl3Djh074HA4MG7cOAwbNsyFkbJr1y52Xn3ssccwbNgwBAQEsOuvXq9nryXqhurr6/HDDz9gwoQJ6Nq1K1doIpGI50REPyYNDtmBi8ViaLVanDlzBvPmzeNDg+z2afEY3XQNDQ345ZdfMHv2bHTt2hU+Pj6QSqUIDw+H3W5HTU0N72InxwG6gYAO36zCwkJkZGRgzJgxSExMZJiqubkZ3t7eKCsrw8cff4yEhARMnTrVpVptbm7G999/j7Nnz+L111/nNblC6w8KLy8v/Pjjj0hMTETPnj3R2trKQ1K6cYmJ9Pzzz+O1115jqCQiIgL+/v58eLW3t2PTpk1455130NDQwO+xkPl0swq/pKQEr732Gn7++Wf06tWLEzXQcYi9+eabSE1N5ZnGvytSU1ORlZWFSZMmYcWKFSgrK8Nzzz3XSWdDTMHIyEjMmzcPR44cYYNFEvUBN6p7YjwRlTkkJIQ7ztDQUPYpEx72YrEYa9euZeKIsIMio845c+bwdUmdPdGIyRXZ6XSioqIC48eP5+tJqIEREjyIkCKRSFz0W2QlRPMis9kMuVzOxpIymYzJKqGhoSgoKEBcXBw7DZBIlEI4N4mKiuKCh/6XZqV0f5E+qkePHvwa6XoFOujR7e3tqKqqclkqRq4R/y6o9fcev+vO5f333wfQUYUfOHAAH330ETZv3oxdu3bBaDRi1qxZDK0IW+1Vq1bBZrMhODgYL730EkJDQ3mrJHHX161bB4fDgS5dumDixIm88c7NzQ0ZGRk4ffo0zGYzlixZgqFDh3IVHRcXh6SkJKhUKp6bkMXM5s2b4evrizvuuINvFLKgOXbsGNLS0lgNr1Kp4Ofnx9U70CGSCwgIQO/evXmwSNsvhVWq0+nE3r17oVKpMGnSJBZYUtIT6ilodS3NJITQ4Pfff4/Q0FBMmTKFDxnqPOrq6rBx40b4+PjgwQcfdLlZnU4n8vLy8PXXX+OOO+6Ar68vmpubXVbakmMy6R3Onj3L6wKcTidXrUI2z+nTp+Hv74/IyEh+rqTIp1izZg1mz54N4MYsh6pvol3TcBvoSMJLly7FO++8g5iYGE5o9DpeffVV9O/f/9+eWCgiIyNZ7Z6dnY0PP/yw089QQSCRSJCeng69Xo+cnBx4eHiwFQ7Q4bws1GQQDKVQKHhvT0REBB+C9JnT9VRTU8PmosLP+/r16wgICGCNBxlNkjULCYX9/f1htVp5+RvRwIlE4O3tjeDgYISEhLCmhq5LKnxovw8JJhsaGlBSUoLKykpERESgvLzcxWVcqVTymoqb76nIyEhGJqj4iIqKQlFREcPQwrkUXXNEkfb394dCoeB15KQ169KlC3r27NnJjWLatGm3ZP39WeN3+060trZi48aNAIBNmzZh9OjRqK+vx7PPPgugYz4iPHxID3DmzBmG05YtW8aHNP1Nm82GtWvXwmq1wt/fH3fffTffaCKRCD/++CPOnTsHqVSKF154AcOGDUN4eDhEIhFvoayurmYCAc0WfvrpJ5w9exYLFizgTkqpVMLPzw+XLl1Ca2srBg8ezGp4i8UCLy8vxMTE8Ou4evUqbrvtNmapUPLy9fVl0RzRfnNycjB9+nTe7UFQV3V1NQwGA6qqqqDT6WAwGHjuQlABAOzevRstLS2YO3cuJ0BiDdntdnz11VfIycnBnDlz2KXWYDDAbDbDaDTio48+gkqlwl133cVwBg1YhYNaq9WKb7/9ll2F6b1ubm6GXq/npWdubm5Ys2YNHnvsMZfrwOFw8FpZi8WC7du3Y+TIkUz7jo2NRUxMDKKioiCVShEfH+9C7vjmm2/wzDPPcJdLYbfb8be//Y3Zcf/JkMvl2Lp1K5xOJ1atWoUDBw7w94hqLIyEhATYbDbU1tayNkYkEjEMS0mlsrISGo2GN03W1NQgOTmZu1g6CHft2oXs7GwAcKHzUpw9e5a3X1JhQPNCWlsQGBgIhULBP0s+eFT5U3euUqkgk8mYxSeRSHDq1Cn2FRNCdTTj8PDw4HnUrboCgpaFIkmJRILY2Fh2gabVzLTamzp9IQmAQiKRQCaTcdFjMplQWlqK0tJSFBcXw+FwoKmpCdeuXXP5PYIg/4qO+N0ml6+++gptbW0YOXIkpk6dipqaGqxYsQIajQaxsbFIS0tjhodOp4NGo0FlZSVWr16NoqIiPPDAA+jatSuKi4tRVFTE7JlNmzahrq4Onp6emDdvHt+c7e3t+PLLL3Hx4kX4+fnhmWee4T31FosFer2eLxwSe9ntdvj5+eHUqVP45ZdfMGvWLG6TaeioVCpx4sQJKJVKdO3alZk6VDHKZDJ0794dJ0+ehNlsxpAhQ9hfy9PTk5XVNCfw9vbmfeX9+/dnryXC04uKilBcXMzwIFVbZrMZOp0ONTU1OHnyJPbs2YPBgwdDrVZzYqVKLisrC3l5eejTpw8GDBjAMJ5Op4PZbMaWLVtQVlaGhx9+mA8q4IZ9OVW97u7uyMzMhFKpRFpaGndePj4+cHNz425RIpHg0KFD6N69O1QqFerr61FfX89MOqJef/LJJ3j00UcZQiG83OFwuCzAIsbPqVOnkJKSwvof4SGycuVKDB06FIMHD/43XtW/HgEBAfjhhx/g7e2N5cuXc+KgwkYYpEGi+Ym/vz8CAwM5efr5+cHd3Z0TudFoRGtrK29OJD1Ye3s7tmzZgkuXLjFVuKmpyeWxaO7XtWtX7kip2ySYymg0oqKiAlarFVevXkWXLl3YyYKgSgrywaPDXqFQ8GPSxlPhrJWSCZnEdu/eHUVFRQBu+IJFR0fj8uXL7K4cGRnJ3UxsbCza29sRGBgIlUrFa8MjIiJ4xfTNQV0L0FE8kh8bhdVqRXJyMrKysv4/Puk/T/wuk0tTUxNWrFgBmUyGn376if2BVq9eDQC477774OnpCZPJBK1Wy0ybL7/8kr3DSKthNBpRXV0Nm83msulw5syZUKlUPPfIyMhAaWkpVCoV3nnnHSQmJsLd3Z1bc7Va7YLje3l5wWQyobi4GOvWrcOwYcMwfPhwAGCMmbY+FhQUYPz48S6vkZIGVXPHjh3DtGnTEBQUBD8/P1atU7dAsEFpaSmuX7+OSZMmseK5qakJJpOJGVc09LRarVzFC1ly69evh7+/P0aNGsXJgJYz6fV6bN++HXK5HPPnz2eRHQ3pc3JysGHDBgwfPhyRkZF8MAiH4YRj6/V6nDx5ElOmTIHT6YRKpWJ4xd3dneEViUSCdevW4Z577mECA81+Ghsb4XQ6UV9fj8LCQowZMwatra2or6/ng62pqYk1Qk6nEwEBAQgLC8PJkyexaNEixMTEuFS8e/bsgVgsxpgxY36bC/j/M/r164dPP/0URqMRf/vb3xAaGnrLSpickAsLC+Hm5saEBiInEHtKSL0tLi6GwWBg2EskEmHLli3Iz8932fl+M4nGbDZjxIgR/N8k8qVto/S7DocDFy9eRK9evdjqn+agRGummQz5zNF6iJSUFGRmZsLX1xd+fn4MMxsMBqbF0++LxR3L8Ii8o1AoEBAQgEmTJuHs2bP45ZdfUFRUBKVSCV9fXwwdOpSLOIKkg4ODERQUBG9vb4SEhPCqcE9PT16/Qd0iOTvcHCkpKew9+FfcOn6XA/0NGzagsbERr776KgIDA1FdXY1vvvkGFosFgwYNQmxsLPR6PWP8EokE58+fx7lz5+Dm5oY5c+awwIoOzqKiImRkZMBut2PGjBlISkpitseOHTtw5swZJCQk4IEHHmDxHbFwyNBReKN7eHjAarXi9ddfR2xsLObNm8fwEP1Oa2srtm/fDk9PTyQkJLhsyyNoob6+HgcPHoRWq2XmW1lZGcNL9NjU2ufk5GDo0KGIiopiVT/t3yCojYRkQrsXOnh27dqF+vp6PProo0yGoAPF19cX77//Ptra2nD//ffD39/fpZJ1Op1Yt24d/Pz8MGLECN7zAcDFkobetw0bNmDSpElISUlhGEZo30GGmtu3b8eIESNcZiUU9D68//77mDNnDjOLqDMDwIlXJBKx59wnn3yCp59+utOysNLSUqxfvx4//PDD//Eq/d9HUVERCgoKMHHixF/9mfvvvx8HDhzAwYMHceTIEYwaNarTzwwcOBBAx0bUhoYGXktA1yd9BuT1ZrFYcPXqVYZXaU4VHR2NqqoqF/EqKdHpeomNjWU4VjgIJyaZt7c3/+7p06cxceJEZnMJZxgE5wYGBsLX1xcGgwGVlZVwOp0ICwvDvn37UFBQwFY9VHgRmUWhUEAmk6Gurg5dunTBL7/8wi4CtHOG3quamhqcP38eDQ0NiI6ORlxcHF8vnp6e6NOnD3vlmUwmpk1HRERw9xUXFwfg1jAhAPTq1QtvvvkmmpqaXODWv+JG/O46l/b2dnzxxRd46KGH+LAtLy/Hli1bMHDgQCxYsABNTU0u7KSGhgZs2bIFQMfyL5VKhebmZr7x6uvr8fXXX8Nut2PQoEHo27cvgI5q+8iRIzhz5gwUCgXmzp3LXkUkTqMb1Wg0ulQwFosF69atg5ubG5YuXcpmgoQZU8dz4sQJ9O7dmyt/GniTvsBut2Pr1q0YNWoU7ywn/zCqSH19fSEWi1FVVYUTJ07g9ttvd6FYikQiZu0IuwwypaTXodPpcODAAaSmpqJ79+4uawC8vLxw9OhRlJaWYvLkyVwdUzKQyWQ4ePAgamtrMW/ePPagIuhN6HVG0JrFYuF5BiVUYskB4Of/7bffYuzYsS674YUsJjKxjIyMxNWrV1kcSSJYOizp+ikoKACATpshW1pa8PTTT+OTTz75jwxe4+PjERgY+A9X4opEInz11Vfo3bs3e7Xd6u/07dsXUqmUq3yinAPgBWtEzXY4Onav0GFKYsq+fftiyZIlCA8PR0xMDFvR3wyN0T1AQ3H6b1K5E0mmqqqKt1vS/SksAOh/qfshm326r6gbotdAhQXNYEi9T6QTu93OtGtyoSCrpB49eiA9PR2tra3YuXMn+5m5u7sjKioK3bt3Zwfunj17Ii0tDWFhYRgwYIAL3PVri98SExPh4eGBc+fO/TMf/Z8yfnedy0cffYSKigocOHCAu4fly5ezEFImk/EOCrJ7OXz4MKxWK2JjYzFixAgWh9FB/vXXX8NoNCIuLg533XUXV3XZ2dnIzMyEj48PlixZAl9fXxZ9CaECgpSIOGC1WvHII4+goKAA69evZ8ovzQHocS9cuACLxcKW6HRTUxKyWq04ffo0rl+/jscff5yH78IhLXUldJOMGzeOcXMhr58OS9plLpx9EHPo559/hkgkwqRJk7ijIb8prVaLH374Aenp6XjooYcAdHQjVqsVOp0OZWVl2LdvHwYPHozo6GjGoOmxhcnFaDRiz549ePTRR3m3Pek0yKGXlkdlZGQgLCyMuxY/Pz9oNBqGApVKJb766ivMnTsXbW1taGho4ERPxopubm48Z3E4HPjoo49uqRt57rnn8Pzzz9+yQ/p3Rd++fSGXy/H1119jwYIFt0xyCoUCTz75JCZPnozy8vJOe9xpC2N+fj5EIhEbkwYGBmL58uUA4LLWAOjomuLj411EskBHkVRTU8PuCjU1NcwKA27smafnKdS+AGDNyi+//MLXhfBapMOZ5jUk8CRmIv3/hIQE5ObmIjo6mq95KvIoadAM0mKxICIiAhkZGbjjjjsYgibhJQD2BouJiUFoaCguXryI8ePHw9vbG83NzVwMCl8rACQlJSE/P9/lsxA6VlN4enoiNTUVubm5t+wu/4rfWedSWlqKl19+Ge+//z4CAgIAdLB9Dh06hH79+iE9PR0tLS3MpnJ3d8f169eRlZWFpKQkzJgxw8XTSizu2OFSVFQEhUKBxYsXIzAwEHK5HGVlZcjIyIC7uzsefvhhfryb7cCpaic8v6WlBYsXL8b169exZ88eDBo0iJ2PafBMbKlz584hOTkZwcHB/DUysKQktG/fPvTu3RsJCQm8C1wYhJ/X1dXhzJkzuPPOO7kqJDYZ+YkJ/Z3oUKCkdvXqVZSXl2P06NEsFiNWEVngtLe3Iz09HT4+PoiKikJAQABv6Vy9ejXkcjmmTp3Kr5GgPfo8qCrdu3cv0tLSEBUVxZ5O1KlRF0ZdzM8//4zZs2dDqVRCq9WisrLSRR9EUBIlaNI+0D8iP9D7e+LECYwfP74Tq4i0F+SI/Z+MLl26YOrUqfj00087fd4U48ePR5cuXfDpp592+l5DQwP8/PxYvQ6AoUZiHwqZe0ajEaWlpQgPD3chk4jFYly/fh3Jycm88yUsLIw/S/qcqAC5+blSN2K323HlyhVER0czbEY0dB8fH56NiMViBAcH8yyR9urY7XZERkYiLy+PbV1IC0OwGHnq0XVnt9uhVCqRkZEBnU7HjhPAjYKHfkcqlWLChAmcWJqbm9HU1ISysjKUl5e7vC56zQSHEWVaSKqQSCQIDQ3Fpk2bGAX5b4mWlha2ffqtRaC/m+TidDqxaNEipKenY968eQA6Zg8vvPACPD09sXz5cmagkLtvc3MzfvzxR4jFYowYMYL3ipDegW4SNzc3PPnkkwgLC4NUKoXJZMLRo0fh6emJBQsWICEhgbcfClXDnp6erBCn9atLly5FXl4eMjMz0b9/f0gkEh7AC3eokL9VUlKSy2EovFFtNhvOnz/PM6SbW36az0ilUhw9ehTDhg2DWq3m4S1Ri93c3NDa2gpPT09mCglDLO7YUd/S0oLk5GQX+IxgpCtXrqBLly647bbbEBgYCLVajbi4OPj6+mLjxo1wOp248847mbpJhoUAXFbNXrt2DVeuXMHQoUOZyUW2NCJRxzbIvn37QqFQ8P6OYcOGsRmhwWBwqYx37tyJKVOmoL6+ngkKZGVC7xFpI9RqNXbv3o3bbrut0/VFO+h/LxEUFIQ5c+Zg7969t/y+WCzG0qVL8c0333TqNoilSKp2gjepSyCqO8HHJSUlcDgcvJee5icikQgXLlxASkoKw6x0nVJQghIq7Sno7xBMSTCkRCLBkSNHUFdXxxAYdSAGg4G9wejv0j1XWFjIcBh1wvSvra0Ne/fu5UKlra0NkZGRMBgMKCws5ARBMyV6fgTdkXEsJT56j1taWjo5HM+cORMfffQR/7e/vz/i4uIQGhqKsLAwxMXFQSaTITw8/L9mQZjT6URpaSkuXbqE4uJiVFVVoaSkpNPqgH9l/G6Sy88//4zDhw/jyy+/5Iv7+eefh1arxYIFC9gSmy4Mg8GA1atXw2w2Y/z48QgJCWH4iqpYd3d3zJ07Fx999BHS09OZh/72228jPz8fs2bNQu/evVnR7enpCalUiuDgYBYvEo3S29sb9913Hy5cuIDMzEyugGnnBFXRJAw8e/YsvL292UFWWC3SoZybm4vW1lYMHDiQYbW2tjbodDpeuUwt/4kTJ/DMM8+wKpuqdeocxGIxU1JJg0CPc+HCBdTW1qJ///4ufl30PpNV+N13381wnVarRVNTE/Ly8nDo0CHI5XL06NHDxX6GjDqFa2UzMjIwfvx43jhIhwUlFvKA8vT0xM8//8z+YEJ/KrPZzPAk0EG9JeiRHps2Lba2tuKpp57CF198gQsXLmDIkCEQi8U4ceIEZs+ejbNnz+Ly5cvsGfd7CpVKxTOQW8WcOXPg5ubGLEkKWgZGegs68Gkrp9Bpm8wwIyIiOvlsGQwG1NXVoVu3bggICGA7Ivo8hF3OzSFMQnl5eUhISGCo2mq14ueff2Y9GDkCBAQEoKqqiq3wCb42m808NxPaylAQKScnJ4evJ7rnRo4ciXPnzsFgMEAsFsNgMHACEa59IAhOLBZzx0+khZsRgzvvvBNZWVku5pRkGyPUv/w3BF0j586dw/Xr17lza2ho4M9fSLP+V8bvIrm0tbVhw4YNeOutt9C1a1cA4LlKXFwcEhMTUV1dDZ1Oxz5Ie/bsQUlJCZKSkjB48GA+1AmTFUIxQgHi888/j6qqKkyYMAHp6eloa2tjfrxcLmdbeDpkqW1ftmwZRCIRDhw44AKtkMiRLlqyszh//jz69OnDXHy6IIWznDNnziAqKgrh4eG830LoFUXJ5tSpU7jvvvswfPhwBAUFQSqVQqlUIiQkhJ0DyJqG3GOFs5i9e/fC09MTgwYNAgCXw+LixYuoqalBSkoKunTpAovFgoaGBhQUFODEiRN444034O7ujgULFvCCMGLXADeMC0UiEQ4dOgRfX1+kpqby16nDIat06i5JSU50YLPZ7LLzw2azYffu3WyUKGSE0cplcjoAgF9++QWLFi3C5s2bUVhYyGsUhg8fjoceeuhXTVD/0zF+/Hjs37//lt/z8fHB4sWLO+kpQkND2eeKKk+CXIWzFuqENRoNs5+EFj5XrlxB165d4XQ60dDQwIcvXYO/FnRw07+8vDykpKS4dDJOpxPh4eFQqVSIjY1FU1MTLl686LJameZzjY2NsFgszHIUFmN07zgcDvj7+0On0/H36FqYP38+9u3bx8QbKs5EIhEjD1SAkVMHXYf0ngiTmUgkwgcffICnnnrqH74P9FncvKb69xJGoxGXL1/GqVOnkJeXx95sBPkRKeZmAse/Kn4XyeW9996D3W7HkiVLAIDXsc6aNQuPP/44zwZo73ZRUREOHz4MuVyO6dOnMzREFybNAuiitFgsqKurwzvvvIMLFy6gV69emDx5sovdOwAWWZFIz9vbGyaTCYsWLUJpaSneffdd9OvXr9Pzl0qlCAoKglqtRlRUFPLz82EymTBo0CC+WYX+SkBH8rx8+TKGDh3KMxDghrWGQqFgg70ff/yRITiaJXh5efHyJLLboF0TBL+5ubkhKysLWq0WQ4YM6TSHsFqtyM/Ph7e3N7tGG41Grn537NiBiooKTJs2jXU/BEmR6yzQcVBpNBqcPn0at99+Oycecuulik8IMW7evBm9evXi7qeqqooTkVjcsev84sWL6NGjB9zd3eHn58daHFpxEB0djeDgYIYBzWYzNm/ejMGDB6OwsJBfY1ZWFoYPH44HHnjgX3rd/itCoVD8w+5l1qxZ2LBhA5uwAh3va+/evSGXyxm+FSYVep+o0KmqqkJISEinv00Gk1R4kf7lH3UrdP0SNEqmrUlJSXzg19bWwsvLC4GBgaitrYXNZkNjYyPDxbSVlZyb6bF8fX3R1NTkMu+h4szpdCI0NBS1tbVc4NCsTywWo3v37sjMzGSol4pMYo5FRETwLMVut7Oeit7Pm1lhMTExGD58OLZu3foPP78+ffrg/Pnz//Bn/hNhsVhQUVEBrVbLHaFQo0TXG511v0X8x5NLYWEhPvzwQ/z973/niqmwsBC5ubmorKyE2Wxm2xaykN+8eTOio6MxY8YMdjomOqzT6eQuxsvLiy+cAwcO4MCBAwgNDcVDDz3Efw+4waxpaWlBYGAgX9wVFRV47LHHIBaLsWHDBhd7dmHQTU6QWn5+PlJSUhAfHw+pVOpyaFKVf/XqVdjtdiQnJ6OyspJhH4KZiDxw9OhROJ1OpKWl8TY+YZVFQRYtDocDdXV1fAhcvHgRUqmU4SJhS5+Tk4Nr164hKioKwcHBbI3e3NyM+vp6bNmyBaGhoZg8eTKcTif8/Px4HwxBD3TY79u3DwMGDOCbWHjDUhdDFaPNZsP27dtxzz33AICLDTutrz1z5gx69+7NcwV/f394eHggPDwc0dHRCA0Nhbe3t8uaXKBDl/Hhhx+ie/fuLu9Peno6li1b9v97mf6mMW7cuF/tXhITExEeHo6DBw+6fH3mzJnQ6/V48MEH0bt3b4SEhMDDw4O3eWq1Wk4StbW1iIqK4hkk0EH7raurY7iQCCBC5uHNIYTCqCu9fv06evTowT5j7u7uqK2tRVhYGFpbW9nSvqqqimnHpN+i65weX6lUunRiwq2UIlHHugCdTgc/Pz8X5X9bWxv8/f1x4cIFeHh4QKVSQalU8gpuk8nEsx4S59KKAaezwxxWp9Px3hl63CVLluDo0aOdbF6EITTF/D0Fia+BG5Y99P9prw4AJgb9FvEfTS4OhwMPPvgg/va3vyEiIgJ2ux0lJSW4cOECq84Jf6UDbfv27VwpRUREuAynqQKiDoaq99raWpw+fRoKhQL3338/CyxFIhFz8iUSCYsG/fz8UFtbiyeeeAJxcXFYv349goKCmOF1c1A7LxKJUFlZyRb1KpUK/v7+vMJVuJjp+vXrSE9P54R1M72Tks3Ro0cxatQonrFYrVaEh4czZk27yGk4uX//fixcuBBvvvkmfvjhB1y7dg1Dhw5lLychpfTMmTMAOkR5VPEBHdXMpk2bIJVKce+998Jms7Eq2s/Pz8VKHehIUpWVlRg9ejQnFiFESPOvgIAAqFQqbNmyBUOGDEG/fv2gUChQX1/PLTp9zseOHcOAAQPg4+ODoKAgWCwWlJeXQ6fTMZRJkCJVXv369cO+fftwzz338OuUSCRYtGgRDhw4gKSkpN/iMv4/h0KhQHl5OetzhCESiTB27FgXvzHh9/z8/JCUlISgoCAXOjgA1p94e3szCYSq/fLycnTv3p2TCs1GhJDTzUGdjdAvrLCwEEFBQS7bX2tra5kVRlsba2tr2ZKGZiz096hDUalULjMkKkjo9URGRqK2tpYtaGg22NzcDJvNhqFDh+LQoUMA4LJts6qqiok3NFei7oV2KDU2NrJeqLS0lGc677zzDt544w0XZt7NYTQaf3eDfSJiAHCx7AFcYUChG8G/Ov6jyWX16tVobW3FI488AgBobGxEQ0MDb2WkJEGRnZ2N/Px8JCYmol+/fi6VOEE11E7TQWm1WvHll1+iqKgIM2fOhEKhcBkyU4dDOgxa6fvJJ59gxIgR+PLLL7kr+LV45plnkJKSgjvvvBPLly+HXC7H8OHD2TtLJpPxh0sJ4tKlS4iOjubnSwwaupkISrh69Sruuusufizhgi2g48IuKytDfn4+Kisr+Sa4du0a2/yTShsAkxcKCgpgNBoREhKC1NRU9nqSSqUoKCjAsWPHEBERgcTERGbctLS08Guhi9NsNmPnzp0YNWoUlEol+59JpVIoFAoEBwfDz88P3bt3Z7bSJ598gnvvvRd6vR4VFRUAOmA+6kyvXLmC8PBwnh0Jky0xp8xmM6qrqyESdWzzvPPOO3Hs2DF06dIFAPiwWrVqFT7//PNbekj9nqK5uRlfffXVLb83duxYhnyEgkWqsgkCIqsV0v84HA7U1NSwrxp1Jm5ubiguLkZsbCyztqgA+Eexf/9+7Nixg92HPTw8UFZWhu7du7u4G1ssFk529M9sNrOtvTCp0MCdGGH19fUAXLskgv7UajVb/xBF2Ww2cxEaExMDvV4Ps9nM1yG9HwTn0txPpVLB19cXjY2NnToPgnmBjuvyrbfewlNPPfWr8NGoUaOwY8eO/9Xn/VuHh4cHIwdU6BGhhopuuVyOhISE34yg8B9LLiaTCe+//z5WrVrFuCpRJwHXzAsAGo0G+/btg1wux+23384XJQCmMlJ1RjeS0+nE2rVrUVdXh/HjxyM+Pp4rdGq7aegn3PH9yCOPIDQ0FG+++aaL5cuvMUVoOHj58mVkZWVBr9djzpw52L17N9zd3V1sOQCgsrISNpsNsbGxkEgkDClQlW+1Wtmmv0uXLqyWJ6+ykpISXgam0+lQX1/PN9WtFqxlZmZi5cqV0Gq1XHVS1zJs2DCuRgl7z8jIAADcdtttnCA9PT35PdJqtXwg7dmzB35+fkhLS+M9LREREZDL5Tw3iomJgUqlgtVqxe7du2E0GhETE4O8vDyUlZUxfk/vz4ULFzB48GDY7XbIZDKXqlFYcLS0tODatWvo168fNm3a5DJTam5uxs8//4z77rvvn7kc/+Mxc+ZMbNy48ZYQy+jRo2EwGHDo0CFUVlaykr2xsRHl5eXM7KN7gCAQT09PNDQ0IDY2lv2z6L2uqqpiUgB1BnTgE6xMQ3A66EtKShAbG8u2+WRx1LdvXwQGBsLNzQ3Nzc2ora2FSqXiwkkk6rCrF7IvqeumTqqtrY2ZlxRUaFEnJRKJUFZWxjZK9P329nbuunr37o1169axWJkshm6OhoYGvPbaa79q7yKEjghKf/3112/5s2FhYb+7oT6xXQnip1UEMpkMYWFhSE1NRa9evf7HguL/Ev+x5PLRRx9h0aJFjI03Nzejrq6O1fH0oRNGuGnTJtZaENMEuHFjUJtNW/YUCgWysrJw5swZJCYmYuLEiVy1C6s42pVCN+zjjz+OgIAAPPnkk6ivr4dGo+GWl4SWN0doaGgnuKyqqgonT57k6oG8waRSKUpLS+Ht7Y2AgAA0NjbyzUIzDLrhyKixqakJWq2WTSqtVivq6urQ0NDAhy39+7VFRdSZEAZfVlYGmUyGfv368Xtst9uRlZWF8vJy9OnTh0VxVOUplUoWsLW1taGiogInT57E1KlTuWukA4rs8NVqNcLCwuB0dnhLrVu3DjNnzkR9fT077tJ8gBhOGo2GmXAymcxledXN84B169Zh9+7dnV6vr68vRo8e/T9fiL+TiI6ORkREBE6cONHpe/7+/hg7diy2Kq0RbwABAABJREFUb9/e6Xt6vR75+fm8uI586GjTKW1xpOE5FVRGoxHR0dFcVADgBE+VLR1M1I3U19eje/fuzMYqKCiAWq2Gn58fgoKCEBYWxgWhSqVioo1EImHYSUgpp4RBxZ63t3cnVp9Q6wJ03IPUsbW0tPDSM3rtSUlJKCkp4bXFwcHBCA4O7vS+BQYGol+/fvj8889/lREm/Hp6ejpycnKQmZl5y59VKBS/K2jMy8uLZ6n0GSiVSiQnJyM9PZ3Fsr9l/EeSS01NDXbu3MlwmF6vR21tLQC4CKzoQs/OzoZUKsWoUaMQFRXFFZGw2qdBHQ2xHA4HfvjhB6hUKjz22GNQqVTMMPHy8oJcLuckRbsgPvzwQ1y5cgWff/45K5aFg+lfuwhp9kNBuySWLFnC+0qotff19UVeXh7i4+M5IVDH4eHhAT8/P579FBQUIDIyEo2NjaipqYFOp2P7fNq/QngzHQK3gn/i4uLYZVcmk+H8+fOIjo7G0KFDWfdCwrstW7ZALBZj6tSp8Pb2RmxsLEOGxKyj+c+OHTvQrVs3JCcnQyqV8hpiPz8/qNVqFppRQr18+TIOHjyI+Ph41NfXu6ynJeX1xYsXkZaWxnMdgi/pvafFTUBH9UlQnDAIOv1vi1mzZuGnn3665fdsNhu+++471iQ4nU7odDqUl5ejsbGRKfoWi4U931pbW3ntMcEhIpEIpaWliIiIQHBwMCcHPz8/7jYJuiLYyt3dnQsiX19fTlBEZTaZTHyw0v0nlUphs9mYjEOrGcixweFwIDw8HAqFgtclSKVS3jskLCKE9Gi1Ws3QOc0OCBp0Op3w9/fHsGHDkJubi9jYWMTFxSEyMhJyuZxnKUBHIn3ooYcQFhZ2SzhSyMykeP311/HSSy/d8hyYMmXK7w4ak8lkSEpKQnp6OtLS0pCeno6EhIROZq6/VfxHksvy5cvx6quv8jCxsbERABh6EQ7ntVotDh06BKVSiaFDh3JX4uXlxQNyYmKRYZ9er8dnn30Gd3d3LF++HOHh4UxnFQodPT094e/vj+DgYJw+fRpbt27FBx98wKItpVLJA3mgY3/GrULoTyQWixEWFsYXISUxunloMN21a1ee+VBlQclGKpXi/PnzCA0N5RW1wnWw5IZMgjP6/0TzFUb//v2xbNkyuLm5wWAwQKvV4uTJkygrK2NDTUrQZ86cQXV1Nfr374+IiAhmhxH8RjoUDw8PXLx4EXa7HRMnTkRLSwssFgvy8/NRVlaGkJAQxMfHIzw8HD4+Pjys3rdvH1JTU5nZZ7fbuUKm15aVlcUbO8ViMUwmE0OdwsVvQIf4k3zQhKHT6aBUKv9vF+l/IGbMmIFt27a5aKEoRo0aBYvFgpycHABgfQjNYcipgHa3NDc386zMw8ODd9Hb7XYUFhYiODiYdWPEBJRKpWxBD4DhKCIAJCYm8n3h5eUFu93OyAPZrJDQWViQUfFHEBnNPuix6SCXyWQ8GxSSCuh/ye2hrq6OkwqdFaTzaW5uxpIlS3D48GFERETAw8MDSqUSMTExiIiIwLZt2xAYGIiYmBjExsbi5ZdfhkajwbZt21ze74CAgE4QePfu3ZGUlMQmucIICwtDdXX1//dn/1uGp6cnz0r/nfFvTy4XLlyARqPB+PHj4XR27OlobGxEZWUlamtr+dAh6Gr79u3w8vLC1KlT2daFBs9UWREuSxfk1q1bkZWVhdmzZ6Nbt26syBVW9rT90N/fH0ajEevWrcMrr7yCqKioTnMLUosTZ18Y1OJTBxAYGIhnn32WyQFkHU4t6LVr1xAbG4tevXrx0DsoKIg7JVq1fPbsWfTr14/ptj4+Pqxcp2qQ2EFCPyYaRALApEmTMHfu3E6wl91uR1xcHFeXJGw7ceIEunbtijvuuIOV1rW1tUyVJjZNY2MjMjIyEBwcDH9/f74J165di8zMTGg0Gi4OmpqaUF1dDbFYjMzMTIwcOZJp1gS/UAep0+ng4+ODsLAwXjHr5+eH8PBwdOnSxcXjyWg0or29vRPThWxs/hsjODgYI0aMwMqVKzt9jyxtTp48yQmFDlTq9MlnzcfHh4e4ZJkjpKNWV1cjNDSULfltNhuMRiPT/v39/fkeoyKM9tQT0cZmsyErKwtBQUEAwJ1Sa2srWxAJ7WTa29tdEg7t4aEuiWz+KVHSvUxuDNS5qFQqNDQ0sD7jZu2NXq9nuPvy5cv8/qlUKowePRrx8fEwGo0IDw9npuWXX36JnJwcrFmzBlKplMkkt4pXXnkFK1asuKUuyWKx3JLV92eNf2tycTqdeOaZZ/Duu+/C4XCgoqICtbW1qK+vR2lpKTQaDTuWuru7Izs7G2VlZZg2bRoPKoXeWYTRC3HZS5cu4eTJk0hKSsLYsWM7XqRYDLlczt5kdFEFBATAYrHg888/h81mw6RJk1wgGIIeaPlWc3MzSktLudMCwDRdcud99tln4eXlBaPRyJYWpPNoa2tDQUEB6uvreWcM0XvpNbW3t6OkpAR1dXXo0aMHz4aIBk0djHAOQTdYa2srD+p79eqFCRMm8OPSz509exZAB4ZMkFxLSwsuX76MsrIyvrmoc2hqaoLdbmdLd6PRiJ07d6K9vR1jx45lwsKFCxfQ1taGMWPGMEQJdPDt7XY7ioqKIJFIXJTiNIiNjo5GQEAArl27hlmzZiEkJISrLYLVIiIiEBsbi4iICMTExCAjIwOPP/54p2usoaEBgYGB/9oL998YGo0GK1eu7LSfvUePHggLC8OJEye4wADgcsiJxWLuhElU3NLSwp8RFTlmsxnBwcEuFb/FYoHJZEJzczPb05PmqKmpiU0xyZ6IuofY2Fh+7Pb2dhgMBigUCh6mk7CTdqkAN4blxGKka1kul/Nzp8KDrgO698PCwlyEwsJ/lKjKysowYsSITqsNxGIxli1bhm+++calSJTL5XjggQfw9ttvo6CgwMUi6eaIiorCqFGj8N1333X6Xvfu3fHjjz/+Lz/xP278W5PLyZMnMXjwYHTr1g1VVVUoLi5GcXExampqYDKZeJUuXaT79u1Djx490KtXL26d6UKlw0k4kDSZTNi6dSukUikeeughvsH8/Pxc6Lje3t6IjIyEWCxGaWkp9u/fj0WLFrENN90EFosFjz76KB+UBDlptVrubmjA3tbWhkmTJrGoj5IF3SRqtRoqlQplZWVISEjgqtzPz89FEd3W1oZDhw7Bz88PUVFR8PT0ZPdY4U1E8yhS6NMujsbGRma8qVQqxo2dzg7jurq6Onh7e6Nr165sD+JwONhiZMCAAXyzk+6E6JoEYZ4+fRqjRo2CTCaDj48PRCIRduzYgdtuu40ZSkVFRWhsbOTDbefOnZg8eTJDn83NzTAYDJz4pFIpGhoaMG7cOPj7+yMgIAAKhYIhQwDsskusQmI7UVy9epVXAP+3xsKFC+FwOLBq1Sr+GsG9Y8aMQX5+PsrLy1mHAdygpQvXWVMnSyav1EGT1xTBWgSrCg95kgKQWWhJSQm0Wi38/PzYmqeiogLu7u4IDw9nyi/QoZtITEzkgT7BvQTtEsOLrieaF9IMlajLlBCFJrJSqRRubm6orKzkIksIuxEpxuns2Ea6fv36Tu+vm5sbnnjiCbz//vsuXx83bhwWLVqE++677x9qWoCOLvKJJ56ATqdDa2srNBoNk2D279//qwy0P1v8W5PLZ599hsWLF0Or1aKkpAS1tbUwGAwujA/i5585cwYeHh6YPXs2VCoV3N3deb+2WCxGUFAQzwuo9d60aRMsFgtmzZrFCm4aRJMPkUqlgkqlYuHkunXrEB4ejvHjx7ss9CopKcHx48fhcDhYgCikNJKSmKp+Nzc3LFiwwGVXCFm5UHIzGo0oLi5m6w5i85jNZiiVSoSFhSEgIAC1tbUYMGAA775oamqCxWJhnjqJ0IRQQWxsLPLz8+F0OjF//nxmWdXX16OoqAgikQjZ2dkAOroaYYem0+lQUFAAf39/REZGMr2ZNAQEVUokEuzduxdqtRr9+vXjqvn48ePw8PDAhAkTuCqtqalBXV0dryLOzc1F//79mTxBB4fNZkNJSQmOHj36q+4DN0MUf//737F48eJOP1dfX88wzX9r3HHHHQgMDMQ333zDxQLdKz169ICvry+uX7/OhAmlUskduXCOBXTMKMLCwhiipWLBYrHwey1MMHTvUSKiIk6j0SA4OJgRBbJnCgoK4mKQ6OyURIQeeU6nk6EmSjhEdQbAMDhBwDabjd01hKSCtrY2KJVKF8sS4XCdXAM8PDzg7++PysrKW8JXvXr1Qnt7O3755ReXr7/zzjvw9vbGY4899g8/o8GDB8PLyws//vgjysvLYTQa+b3t0aNHp/nNnzX+bcnlypUrCAoK4r0dVqvV5fCiSqa9vR21tbU4evQoHnnkETaUDA4ORkREBEJDQ+Hn5weTycSDfblcjitXrqCyshKDBw9Gr1690NraynseSIFMFyl1OlVVVThy5Ajuu+8+iEQi6HQ67kTI0mTw4MEQiUQuswXghmZFLBbj+PHj6N+/P9zc3Bi+ogtcKpW6KH9tNhsGDx7M7DWZTIagoCCEh4ezgeXp06cRExPDVRgJGPV6vYtlOFWRdKBv2bIF3t7emDx5Ms9wXnnlFXz66adYuXIlCgsLERAQgF69enUSpzqdTvZNoxuX7ESIuVdcXIzr16/jtttu4xu/paUFe/bswYwZM1xs1EnwKJPJsGPHDnTp0oVV0dSFCXe7ZGZmYuLEidDpdMw4AjoqYeFwnt5LgmMo8vLyfrcK/P9NeHh44IEHHkBtbS02btwIrVbLn1Xfvn3h4eGBw4cPw8/Pj2cLxKCjmQu9pzKZDO3t7fD09OTPhbpVYdUvdD8W6lronqmrq0NoaCiLgNvb21FdXc10VnJXEHqaCUXQRLqhg97Ly4tJMDQzotmPw+HAxYsX+Rqnra7keUdiSJPJ5GLvRElFCEXLZDJkZWXdkiAxffp0LF682OV7Pj4+WLduHX766Sds2rQJQMe90NDQgIqKCmg0Gt4nNWPGDPz444+dmGPjx4/Hhg0bfpeWMP/u+Lcllw8++ABPPvkkH74kFqQKXBi7du1Cv379EBUVxVAIqYKpe6HfkcvlaG1txbZt2xAXF4fp06dDLBa7tPbl5eXcmlNCA4AtW7YgOjoaU6dO5a/TbKGtrQ2XLl1C//79GZMWBsEAPj4+yM7OxogRI+Du7g65XM6QAO3UADqoj1euXIGbmxsCAgL45ggKCuJqu7q6GocPHwbQYZxHN7uPjw9vG6QuhjoJT09PtLa2Yv/+/QgODsaUKVPY5oJcoYGO2QfNj06ePMnMlvb2dly4cAFubm5ITU3lxEKHiHBHyPbt25Geno4uXbrw4DgzMxMJCQkYOnQoQxMA+L232+3IzMzEsGHDXNYayOVyHkq3tLSgsrISiYmJzMsPCAhAdHQ0ZDIZqqurUVFRgcbGRnz77be3ZIhdvHjxlnqG/8Z48MEHIRKJOlFkJRIJBg8ejOPHj6O5uRlBQUHw9/dnSi+tYqBDnpIK6agA8AF986FIMGhzczNX4fRztbW1CAoKcklEdrudrXfosahok8vlrKgnsoq/vz/b9vj6+jILkQoXSn56vZ799ChRUZFlt9sZTtbr9fDy8mLoVMgABTpQg7CwMBQWFt5S4NitWzcMHjwY33zzjcvX+/Xrh+effx5r1qzByZMnceHCBVy5cgVFRUW4du0arl69CqPRiBkzZuDs2bMsoaAYPnw4Tp069bvSvPyn4t+SXMjRNSoqipOCUJEtrJiKi4tRUlLCw3iHw8H76AkXBuACh61btw6tra2YP38+fH194evry8vCiDFDlu9AR7VUX1+PjIwMjBw5kneICIekhw8fRt++feHl5eVySANgW3sAKCgogE6nQ9++fXnIKLzACY4gK3uqPu12O3Q6HaxWK2pqanD16lVUV1cjNzcXSqWSTfyIVUMJsaWlhbsVIXyxdetW5OXlYeLEiWhtbYXBYHC54YVx6dIlfPPNNygrK8O5c+fg6emJvn378vyE6KdCCvS5c+eg0+kwbNgwF3eEAwcOYMiQIcxOIto10NHVnTlzBhqNBiNHjmT3ZhoUAx2uA6dPn8aQIUNc3j8fHx/o9XrU1NSwDqi8vBynTp3ipVQUWVlZSE5O/v+/QH9nERMTg9mzZ8Nms7kcUm5ubhg1ahTMZjMOHDgAjUYDo9EINzc3REZGIioqCqGhoTw3pNkBFWMEj5Ht/M12JjQDIYo80FFk1NfXIyQkxMUPTK/XQ6VSob6+HvX19aw9oaV1pNkKDQ1lPU1bWxvfTwSNEXTs7e0Nh8PBxJlz585xEUpOxkDHbI6s92UyGby9vaFWqyGVSrm48fLy4pllZWUliy1vjldffRXvvvtup2Vst99+O/bs2YNXX33VxeuMzqKioiL0798fwcHB2LNnj8vvSqVSDBw4EGvWrPk/XQN/hPi3JBfqWoCOlthkMvEFT50LDW337t2LUaNGQa1Ws+0IAPadooREti05OTm8/jc2NpYPN8J0vb294XR2WE2TPYaXlxfOnDkDp9OJ1NRUFoEJ1cFHjhzB0KFDGTum9l6hUPDiJaDDb6l3796IjY3lxKZUKqFWq10WFbm5uSEnJweBgYEuLbNGo2GKc3t7O65du4b4+HhYLBYXbybCxOkAJlijtbUVly9fRk1NDQYNGsSaHl9fX6an/lpIJBJcunQJOp0OYWFhcHd3Z7sX4bY+Nzc3HD58GIMGDWI6cEBAADIzMxEcHIxJkybxigKh6peElmlpaairq0NjYyMfQITzW61WHD58GD169GC6c1NTE+rr6zsNVn/66SfcfffdLmw9ALh8+TJSUlL+V9fk7z2effZZnDlzxsXyXSqVIikpCQEBATh06JALDZ3sU0JDQ+Hu7g6tVguz2QxPT0+XXSl2ux1hYWEAOouCyUCSdthT901Kb6EXGO20p8/TZDKhtraWmZNEoGlpaeGEQP5iBOVREiC9WkVFBRNWaNkY6XMAcJIJDQ1lRht17xTU0SsUCkRHRzPr7lazl7CwMMyaNavTcL9Pnz4YOHAgfvnlFzQ0NHT6PbLrnzJlCvbs2QOj0QitVssuBAqFgi2U/szxmycXUuLSet3q6mq0tbVBr9czL57UtTk5ObDb7Rg6dCgnFhqQU6dCFQz5EX3zzTeIiIjA008/jfDwcN77Tngsie/oRqIWOjc3FyqViqtg6iba2tpQV1eH/Px8Jg1QJR0REYGgoCCX3Q8HDhzAuHHj4OPjw1U5qaWFGDNBbrQMjd4b8oQiSKCmpgZdu3Z12UsB3KAbCymY9H7Qqty5c+fy36YEcytfJYlEgrvuuotXDXt4eKBnz57w9/fnvSnC5HLw4EFIJBJWNAcHB8Nut2PXrl1YsGABOzsLBZHu7u4wmUw4fvw4hgwZAqDjBq+vr2f8vLm5GQUFBQgKCoJMJkNVVRUuXryI8vJyXL58GQUFBQxHWiwWFBcXIzk52WUmc+XKlU72+n+ESE5OxogRI7Bu3Tq+jihx9+/fH2fPnuVuvr29HU1NTcjPz0dRURHa2tp4DuHl5cWKeLoHqAsGOicYEkmS+wUlJV9fX4apSJR589I4s9nMa4YJgm5qaoLRaIRKpUJLSwuio6ORkJDAK8eF3VBWVpaLt+D69ev5nqTEaDAYEBAQAKvVyjNHIuuQYLmtrY3PDq1WCwC/KiB89tlnsWbNmk4CyAULFsDhcGDHjh1McqCikJ7zyJEjUV5ejm3btrEfYHNzMwYNGoRTp079quj6zxK/eXI5fPgwQ1xGoxEmkwnFxcXsMUQfFO36njFjBmQyGe/2IE8sNzc37gpkMhkCAwOxfv16aLVarFixgpd1iUQibN++HdOnT+c1pe7u7ggLC4NSqeTO5cKFCxg0aBBThr29vdn87sqVK4iMjGQ3VWrbb961bTQakZWVhXHjxrFtBvlkAR1wBJn7Xbp0CQCQlJTEyYHWv+p0OrS0tKCoqAjh4eGIiopiqjF5eQFwMRKkzqyiogKFhYVITU3FwIEDuVOjaupmOxipVIonnngCAwYMYPZY7969kZSUxCpe6vRaW1tRV1eHQ4cOYebMmfDx8WFh6MaNGxEaGsoMMNrgScaGDocDZ8+eRXR0NJKTk5nAAQC1tbWsHCfvN3q/hAdha2srysrKYLfb8fPPP+Puu+/udH1lZWVh4MCB//cL9XcYS5cuRW1tLVavXs3Xlk6nw+DBg+F0OnHo0CHe4kh2QMQaI4q5p6cnkzKEy8CAG7YqwI2dO1SQ0X3mcHRsgFSr1Qw3u7u7w2w2c3IhajH9vtFoZCEmwXFqtRpqtZrFm0TfJzi6ra3NRfTocDhQWVmJc+fOAQCTEICO7qSgoAB2ux1+fn7MmlMoFHy/UJdkMBiY7XmrkMvlmDJlCmbPnu3y9WnTpiEgIAA7d+5kz0O9Xg+TycTJbMCAAbBarThy5AjMZjMLQJOTk+Hj4/OrO3r+LPGbJ5eLFy+iZ8+eAG7slyAMXViF5+XlQSqV8gdDdEago5pSKBSIiorCsGHD0L17dzgcDpw+fZrX/4aEhCAkJAR+fn44duwYnE4nEhMT2cSPmFsymQwVFRUwGo0YNmwYV/YEsykUCrYRDwwMRHBwMAICAiAWi9kcj+KXX36BTCZDXFwcDyjpddFNTF1TQUEBAgICeO5A+y5aWlq4AyEbfB8fH+7oiLFDcykhtOfp6cn7Kx5++GFeuGSxWNjTTNjWBwYG4qWXXkJcXBy8vLxw8eJFAMCQIUO42qW1yWSpfvToUURFRaFbt26orq6G2WyGRqPB/v37MXPmTEgkEh4SU+VIO95PnDiB5ORk2O12F5+mt956C8888ww2bNiAgoICpKenu6zopcQDgNf0Xrt2Db169QJwYzFaSUkJoqOjf4Or9vcRkyZNgkQiwWeffQZfX1+GZgMDA9G3b1/k5+fDbDbzPUUVttFoZBJKUFAQM7Lo2qVriaBV4Z4P+qfT6WCz2dDQ0MDzRYKZgY7OgqBrobW9QqGAVqtlaIoKwuTkZJSVlfEGSkqKlJzy8vJc9o8QHH306FEXirGnpycCAwNhMBgYsqW/Qc+DEgyRWm61hVMYo0ePxtGjR5GXl8df8/Pzw7Rp09Dc3IyTJ0/y11tbW1lv5+XlhT59+nCRRqw9q9WKtLS0Wxqq/pniN00uWq3WRfdBzBODweCyObKtrQ0nTpzA0KFDGeulr1Pb7uvri4iICBZAZmRkoK6uDsuXL3dhlXl7e6O4uBgDBgxAcHAwevbsyV5VpPC9evUqxGIxV/o+Pj5cxfn5+aGkpATjx49HWFgY/Pz8XIbiQhiBti+S3b3QUZYSCFXhpaWlvKNep9Nx10Y3q9PpREVFBUJDQ5lBIxSZkRUHrQumqrKlpQUjRoxgB2DynGpra2NmGdBxwz799NNc3RUXF7OaOiUlhdlfROGUSqWoqKjA8ePHMWrUKEgkEiYS7Nq1Cz179mQDSqvVCrlcjujoaN7rodfrcenSJYwbNw7AjX075AnX0tKC06dPo6mpCc899xwOHTrkAvtR1wgA3333HaZOnQqgowslGuuGDRswcuTI3+Ta/T2EWCxGQkICbDYbhg0bxrvpHQ4Hd55ms5mZjrR9UEiv9fb2Zk2WcLWBkLIMgKFMsVjMKxUoyRBLk+BhOlzVajVfNwSfyeVynq8B4CLIarVi0KBB2LNnD+rq6lwKMCKCJCQkYPTo0Zg9ezaUSiWeeuoprFixAj4+PpDL5QgMDGR2JTE6ifRCjhg+Pj6IjIxkJwfS0PyjmDhxIsLCwvD111/z19rb23HvvfeiW7duOHv2LLsfqNVqKJVK7gyHDBmC9vZ2nDp1yuVv9uvXD3v37v3VHTB/hvhNk8u2bdswbdo0AB1uqVVVVazKJgzVbrcjOzsbiYmJrDInzj7NDaKiotCjRw/4+fkB6KDVrlq1CnPnzu2kd9i1axeADsw0Pj6eW3hqzc1mMw4ePIi4uDi2naGBp1KpRHx8PMrKyniHijAIc6bIy8vDqFGjYLfbeRsdmUMC4BuHDPWSkpLg5ubGMAWxzojxRdoBqtQowdpsNnh7eyMkJISJAkDHnpZr165h0KBByM3N5VWtxATauXMnP9epU6eyrYXNZkNERAQef/xxzJkzhw97rVYLg8HAa3IzMzMRHR2NxMRErm7r6+tx6tQp9OnThxOlwWCAXq+Hj48PoqKi0LVrV+Tm5iItLY2tWIS27lQJEz28oaEBmZmZDNcQCYHw84KCAowePRoBAQFcSBQXF7ND9h85iKhQUlKChQsXorS0FG5ubuwNR3NKALw3hboPoAP2IcYZdfHUudDvEN2dDCmBjmudjEtlMhmTcJRKJRITE9GtWzeGZukad3NzQ1BQEG+HpAE3rYxIT09HRkYGC5gpobm7uyMtLQ3Lli3D5MmT0adPHwwZMgRlZWX89729vdmyiQwvtVqti20SMeccDgeTa4TzuV8LEkB///33zCqz2WwICAhAQEAAzp8/j/r6esTFxbHehzr9QYMGQSwW48iRIy5/c+jQoejZs+c/XJH8R4/f7M4k3N/Pzw9Op5Mt44UzBKCjzczJycGAAQMA3Njf4OnpiaCgIKSlpSEuLs4FM/3yyy9hMpnw9NNPo729nb2OjEYjNm/eDA8PD/Tu3Zurajo4L1y4AK1Wi9LSUj5oCcqiCz0nJweJiYmdBuHEkKIwm83IyspCSkoKGhoa+GBubm6GTqdDdXU1w2hki07Jxdvbm294qgT1ej2sVisnFwDc7pOv0uDBgxEUFMR2HYcOHUJoaCiio6NhNBpx/fp1VsCLRCK+sN3d3TF48GAmB1CX16NHD17KZbVaYbVa0dTUBIPBgLy8PFy4cAHjx493WSKVmZmJkJAQpKSk8ONYrVbU19cz55/cEubNm8dzMJFIxDM0oYs06RE++OADJltQEQF0FCgvv/wyIiIioFKpuNLeuHGjy4bOP2oQzOp0OmEwGPDiiy8iJyeHD+Rz586xqzXBZgSPAR1Lo4h1R/cddb1UfVNnrFQqea5CgkyLxcJJRyzu2JBKj9GnTx8WF1OHT+7WxB6jAb27uzsvGKOuRjjv0el0zABra2tDSkoKcnJyoFarER8fz7M8Pz8/Lt4OHjyILl26QKFQMCmG7Ibq6up4DvLPxIIFC9DU1MTiSYJlZ86cCQDYvn07JzIq/mQyGUJDQ9GnTx+cP3+eNW1ubm7cxZNu7c8Yv1ly0Wq1mDx5MoCOA9xsNqO5ubmTaPLMmTPo168ft+keHh5My73VbueWlhZ8/PHHuOOOOxAWFobS0lI0NDTAYDAgNzcXly5dwoABA1BbW8tGmFQhm0wmVFdXQ6fTISQkhO1gfHx84O/vD3d3d5w4cQL9+vVjNgsddpGRkS6GdqdOnWJbF41Gw/tJCPMlzr7JZGIzPIVCwQc8KdSBDvya2CoREREukAFwY9NmTU0NV5nnz5+HTqfD0KFDXYSLtICroKCAb8KpU6eyNxth1KRfoaqSPh/aiZOZmYnIyEhOduQxlp2djTFjxgC4oa4GOlZUFxQUwOl04vTp06irq8PcuXPRu3dvpKWlITo6GvHx8YiKikJERASAjgMnKSkJGzZsQHJyMpKTk5nmTW4HBoMBEydOdLkGKisrERIS8ptu0fu9BF2/ADghfPzxxzhz5gwGDBiAsrIyNDc3u8z4CF4kTzuCxehrNGuh64CW5en1emZfETNLKpUytE1EEnJapmKBEj4VaUqlEmazmY1Pya3C4XAgPT0d58+fZ3IB2TMRmgGAt5dKpVKcO3cOI0eORJcuXbirIgiKtD6hoaE88yOmGz1fISz/jyI8PByTJk3C1q1bWd8lk8kwdOhQBAYG4pdffuGk6Ovry4VQSEgIJk2aBH9/f3ZOCAgIgK+vLyZOnNhJB/Nnit8suRClFrhB3SPvMKH3kc1mQ2pqKlc9VK3LZDKmuApj27Zt0Gg0ePrpp6HRaFw0I0TJJct1u93OlbjVaoXRaGTWllKpZPGUUFBWXFzM1hnh4eGIjo5GSEgIX9jEYMrIyECXLl3YMZnMGNva2iCVShEcHMy/U15ejtjYWIYATCYTfHx8EBISwu7DtBqWNmnSDS40aywpKWEKd2ZmJry8vNCjRw9m3xA9083NjYeQnp6eSE9P5wErqeSJlSX0lyKcvaamBnl5eRgxYgRDLO3t7di1axdCQ0ORnJzMA3jatULOyZcvX8Y333yDRx55hDdqRkZGIikpifHy+Ph4AB3Qwblz5zBo0CCkpqYiLS0NAwYMQK9evZCSkoJNmzbhjTfe6HQN/PTTT5g1a9b/5fL8r4mbtT6UFJxOJwIDA9GzZ09cv34dQUFBvLmT4FtSuQu3utI/ut9oziJkGNL3TSYTPDw82GdOSDVXq9W4evUqE1YI7m5tbUVcXBw0Gg2kUilDm+QrNmjQIJSUlMBsNrtsURUO7YGOgmrq1Kn49ttvodPpmFGam5uL0tJSfj9eeukl7niEe2SADkgwOzv7n557zJkzB9u3b2fDy+DgYCgUCtx5551ob2/Htm3b4Ovr6+JfFxISglGjRqGurg7Hjx+Hr68vQ9gTJkxAZmYmJ/c/W/xbAGtqsYUDejc3Nxa/UUIRWmwTNlxVVcWePgDw6aefYtasWUhOTnZhbrW3tyM3Nxc2mw2BgYEu37NYLHwok6hKrVajtbUVFosFH3/8McaPH48xY8Zg165d2LRpE+Li4jodYPR8LBYLcnNzmbVmMplY9EgJhmwu1Go17x5vbW1l6iV1CP7+/jwYT0xMZCowwRVk2Ecqf51OhytXrqCwsBBpaWlc+VFyoOTR0tKC+Ph4DB48mOncxNqiXS3CYT/NvxwOB44cOcIDe8KwdTodSkpKMHnyZB7SCvfo0AFWU1ODrVu3YujQoUwj1ul0TDLw8fFBTk4OgoODsW/fvk4UUXd3dyiVSlRVVaGtrY39zihaWlrYkv/PEMLkIhaLMW/ePBw4cAD3338/QkNDcf/99yMnJ8fFSfrmap00ZvQPuLF2W6hnEhYStNWSriVyZjYajZBIJJDL5aivr2f6unDuk5qayoe6RCLheSSp8sPCwrB582Y20aSfIwoxXcsREREICAjAG2+8gfz8fDQ0NGDDhg0u2poLFy7wBkh6TRQKhQIOh+N/dDmmuP322xEdHY2VK1fycwoLC8PSpUshEol4h5FwzkeQc79+/XDhwgWIxWJeHx0XFwe73Y4nnnjif/WZ/1HiN0kuJpPJZWZBFFrqEOgw1Gq1Lh8WHRhOpxPe3t58ABuNRpSXl+Po0aM4ffo05s2b59KxkO11YWEhgA5oSWjn3tbWxuZ6pFWh1p1a+erqaraKMBgMKC0t7WR419zczIK1K1euICoqim8E4WPR821paYHZbEZjYyOio6PZyoLMF1tbW3H9+nUUFhaiqKgIKpWKkyxBh2R5Q3+rra0NpaWliIuLw6BBg/i5Edzo4eGByspKXLp0CVqtFvfffz/re2jwSo4B1LUAN/DvyspKXrh1/vx5aLVa+Pj44OjRowgICMCUKVNcbGUoKRH54uDBg0hISICnpydKS0tx/PhxlJeXM7Vap9OhqqoK69atc6kyb44XXngB8+fP7/R1Ly8vzJkz5x9fgH+gSE5ORt++fbFu3Tq8/vrr0Ol0fOjGx8cjNTUVfn5+yM7ORnt7O0JCQng1NXW/ZE1EnzElA4KmCMYlUWJLSwuampogEol4OyTBZ4Q+yGQyaDQa/v+0k4gO14qKCgBg8gq5MQDAyJEj0draigsXLnARSS4DQm87m82GiRMnYv369Wx4un///k6dyEcffQSLxeKyTA4AU5NvdnT4tXB3d8cLL7yAwsJC/Pzzz/z1+Ph43HfffQgKCsLmzZtv+bukwbqZfuzm5obvv/8ex44d+6eewx8pfpPkUlpaipiYGP7v2tpaNmqkDz8nJwd9+vThVpY6FplM1kk1TPHJJ5+ga9euGDNmDPtgAR3VXXt7OyorK6FUKnmnSl1dHRoaGthp19fXFy0tLUhMTOTWtr29HY888sgtWUevvfaay38TLpyfn4+QkBCoVCqX3eAkAiQ7mZKSEhw5cgQikcilK7Hb7WhpaUF2djYqKytRWVmJpqYmHtbT4U2zDhIuEuvs7Nmz8PPzQ1BQEFeD5KXW1tbGLLHhw4dztUpOtcJNfyRMo3kX0LFzx9fXFykpKcjIyMB7772H119/HcePH8fo0aNRU1PjYqlODtChoaFwc3PDjh07MGnSJD4wqGqlgyU/Px9eXl7/0K7l2rVrqKio4NnOnzl27NiBc+fOYfbs2Vi0aBEsFgvq6+t5xmK32zFlyhTs27ePr+/6+noAcFmrIGSQ+fr6slU+dbF0sJPYlQoQLy8vptlTp+Hp6Ym4uDiUl5czu1OhUPBjisVidOvWDdnZ2UxtJ787EkPPmzcPx48fR21tLa/GoO6JFpZZLBYEBwcjMjIS27dvx9GjR3nlhjBMJhPPPImx2draCpFIBLVa/U8nFwCYN28eIiIiuHuhmDhxIk6dOoV33333lufTnXfeCaCDaEKh0WgYrRk/fjxyc3P/6efxR4jfJLkIBZAWi4XFUzRQFolEDAkJGUKE3zudHeuPhR+iTqdjuxGq7pVKJVfilZWVaGtrQ0xMDA+qqeInBbOnpycqKipw/fp1tu9Xq9Xo27cvFixY4IL3Tp8+vdPiKWLPFBcX8w0uXBdAw3rhHKOiogKJiYn8c+3t7WhtbUVjYyMaGhrQ2NjITgJkN0NzKcLWaRbS3NyMhoYGXLt2DT169EBAQAAiIyMREBDAMx6z2YycnBy4ublhyJAhXGkSk0WoyibIjbB5s9mMK1euYNCgQS6MrerqajgcDqxbtw4HDhzgG5x0Oh4eHjCZTEyHJuafcNsfzbe2bduGO+644x9SRD/88EM88cQTndyy/+zh5+eHyZMnY+XKlWwC6+7ujtGjR6O4uBjl5eWsMaGBOnUsws5FJpN12pQqZJwRLEs/S7M80oxYrVZ06dIF+/bt4+6/vb2dNVTk2ZednQ03NzeX5WUOh4Ph2Pvvvx9r165Fc3Mz+4vRHIYKuba2NiQkJGDfvn0IDQ3F3XffjUWLFmHAgAGIjIzEtm3bcPbsWXTt2hW1tbUu5CFPT0/079+fEY1/Jjw8PPD8888jPz8fW7Zs4a/fcccdiI2NRXZ2dqc9MAAQGRmJAQMG4PTp0wy9X7x4kWE60qPRvf5niH95cnE6nUzZNZvNyM/PR21tLbv7urm5IT8/H2lpaTxTILsXEoMZjUYYDAYXD6RNmzbBzc3NZQ4SEBCAwMBAeHh4oKSkBECHvUpzczNjpl5eXggICIC7uzt0Oh10Oh1vbiR8Xy6X48UXX3RpyW/uWoCOpGMwGFBYWIigoCCXqvDm/6X/X15eDqlUCrPZzINRIbbc3t6OqqoqHgRSByQcsJMJoMPhQH5+PoAOuISM/6Kjo9l+5fTp03A4HOjXrx/vjCF6J0EgpE+gQSpZ0WRnZ8Pd3R1Dhw6FTCbrxMYyGo3YuHEjU47pQKCZy759+zB+/HiGu4gNBICTWGlpKa+TvlU0NDTg4MGDuOeee/65C+5PFgsXLsT169dx8uRJuLu7M5Q6atQo7Ny5k3UuVMgI1wZTkGJdrVa77LoHXK/htrY2+Pr68moM4P+x9+fxbZ7XlTh+AJIACWIhCBAECe6LVi7arM2SN3mRt3jLbidO02aaJm3TppN2Mv2m02mbpmmbTqedtPlMmtVZXNuJY8dLbMuLVlu2FoqSKO47CJIgSBAAsS+/Pzjn6nlJyrESOT874f189JHEBXjxvs/z3HvPPffcReia9cuCggKZIhuNRlFYWAiHwwGHw4HW1lYRc4zFYtKYqQZeNpsN733ve/HVr34VR48e1aikU9w0lUqhpaUF0WgUo6OjuOeee3D33XejtbUV0WgUa9euFYo0M3iPxwOn04l4PI6ZmRlNv9dbsY9//OO45ZZb8LWvfU0CJGrrAVoNP9W++MUv4tChQyIMevr0aTlTcrnFcdF79+5dJtP/62pX3LnMzs6itLQU6XQaExMTmoid+kd6vR4Oh0PYRFQ7Vg+chYUFjXRJZ2cn7r333mVSDpRoCQaDaGtrQ3t7uxygRUVFcDgcgr3qdDq5PuoDlZeXQ6/Xo7q6WvSF9u/fv2zw1NzcHMbGxoQS7HK5hDrNfg8AsiHInGFjJCM/sl7oWHifKAZJp8KMTH29dDqNzs5O1NfXw2g0Cuw3NzcntGM2c33gAx9ARUWF4O7qpEIAwvRitphOp3Hw4EFcc801ItCpFs3pgH7/939f7l9JSQmKi4tF6fn06dOiFABAcHhgcXP29PQgmUzC4XCsSDMHgH//93/Hb//2b//GFOwv14xGI774xS/iT//0T+VZzs/P45prrpFJpAyqVNVx1WmUl5djenoaLpdLaqOqICz/sJM/EolIbxX7uBKJBHbt2oWXXnoJFRUVKC4uFsSCxJWdO3dKlzr/kNDC2o7b7canPvUpzMzM4N/+7d8wOjqKdDot6AQA6YQ/cOCArGO32w2dTifOi6Y6QgDYvn07Dh48eFnDu4xGIz784Q/j5Zdfxr/+67/K1z/xiU8AWIT5v/zlLwtJhXb99ddj79694shVIU4Aok+4tOHy19XeFlhMp9MhFAohm82K8mkkEpGej+rqak2WoB6mPPQByEI8d+4cTp48iS9+8YsaSXe+V1lZGc6dO4fOzk5UVFQI1Mbu97y8PJhMJuHzu91uVFRUSMcvxSNZlFsqkMgucgDCkqmsrJQ6EXsLVMICsFgLikajyyRdGMXzTzwe1ygN8OvqhgcWYabe3l5s3LhR7ls6nZZpjX6/H263GzfeeCNaW1ulgTQvL0+Uh7lp2cFMp3j48GGUlZVh586dWFhYwNzcnIZJ5HA48Ld/+7dob2+XCJGZUF5eHl566SVs374dFRUV0jfEQVHAYrf9wYMHce2116KoqEgzXVK1U6dO4ZOf/OQvsux+Y4yNfdSVY1G9tbUVvb29st45Ywe42JBL/S82FjN7ZbZDJ8TstrCwENXV1ZienpZ9TKLN2rVrceTIEYGo9Xo9otGoZE/bt29Hd3e3SA2l02mhLjNwAhbZpHfccQc+/OEP4/Dhw3j++ecRDAaRzV6cILt79250d3cjEomgsbFRmpf7+/s18LmKGgCLCtKBQEB0w8iEC4VCb0pRfuCBB7Bjxw781V/9leihzc/Po7KyEgDw53/+53j88ccxPDx8SYj37NmzmvPsi1/8IgKBwG8Mjf5toyJz4bB2wL6KhYUFGQwEQPDepbUAtUHr29/+Nn7v934PNTU1K76XzWbD+Pg4AKCurk7wZTKzmLJzMbG/pLq6GoFAAKOjo/D7/dKXslQMkZkVIbupqSmUl5dLIZ9ORs28crkcvF6vaGGpC16FKDhDXu3pUZvgVPx7enpaxhcQ5kqn0xgeHsb4+DgOHDiAnp4e3HrrrUJiUGXTWXh3u93SNGo2m4Xl5fF44HK5EI/HEY/HJSprbGzE//f//X9CgiCUQiWFRCKBJ554Ap/4xCewYcMGlJWVSeRss9nQ0NCAqqoqHD16FO973/s083BUGx8fh8vlWrG/adUuml6vx5e//GV84xvfkF4VALjpppvwwgsvyIwUzkxhwVxt8KVkEQAZL6ySNACIMsS2bds07CkGFcDi3uvs7JQ+MpXdCCwWwp988kmpNabTaZSVlcmUSnWf22w2fPjDH0ZNTQ2++c1v4tChQwiHw9JX8/GPfxy1tbWoqalBc3MzdDod+vr6NKKoPG9o1A88ePAgZmZmMDg4KDOUBgcHL9mDotfr8S//8i+ifTc6Oorx8XHs3LkTwGJQ/Lu/+7v4v//3/4rk0lL7+te/jlOnTmFubk7qqSQ+/CbYpbmgv4Cp0x4Ja1CbiPhsRUWFKCGrXbqqRhB7VViDOHjwIL71rW9d8n0XFhYwMjIiB6bJZMLU1BRyuZzMdUkkEigpKUFbWxva2tpQXl4Ov9+viTp4zRxKxEVKHSOm+3RMwGKEl0gkpMitRkP8vCpsBkCTonNhMiJaOq+FkSXFJpPJJGw2m9CaY7GYZGUHDx5EY2MjHA6HFNCJp5Prz0yOUhqhUAgHDx6E3+/HJz7xCTlcKIfucrnwmc98BtlsVtRumQFy9PLLL7+M9evXo6WlBW63W/TcqExrMBgEuiMtdiV78cUXNbDaql3a9u3bh9tuuw0nT57Eli1bcP78edhsNrjdbgQCAZEfCYfDCIVCy4RVSalXoTPgYmDDdcL5Lj6fDwMDA9K7QeexZcsW/OQnP8GnP/1pyXbC4bDUKmpqamA0GtHT04NNmzYJzGu1WoUoYDAYNCMm2tra0NjYiH/7t3/Ds88+K3vYbrejs7MTDz30EB588EHU1NSgr68PxcXF0i/DLEGF4t73vvcJ21G1bDYrRKOVYNjt27fjwQcfxHe+8x3s378fZWVlGseVy+Xwj//4j+ju7sa3vvUtjawRAI2o6p133onXXnsNsVhM04vz62xXNHNRbxw7zalFVFBQgGg0CpfLJYctaxaUIgEgDCzCPV/96lfxW7/1W5oHxx6ZyclJTE5O4tChQ8hms/B4PDISmA1ibACjfldnZydcLhdGRkbQ29sr8J1qlG5Rjdjp7OwsRkdH0djYKBkYu+JVlhyw6FjZja7CD6pNTEzITAr+3NI0nxu+v79flF7pXDiC+fz58/D7/di5c6dmWBQZPsxyyKbhPTebzfjZz36GlpYWVFVVifM4efIk9Ho9Pve5zwmERil+wi4USnzsscfw6U9/GlVVVTAYDNIfYbPZhIH3/PPP4+abb16W3amf9eWXX8b1119/OUvuN9r+/M//HF//+tfhdDqxZcsWuN1ufPCDH8QTTzyBwsJC1NTUiHAocFEVOZVKwWw2Y25uTtPnpDoUBn16vV4YTg899BCGhoakZpLJZFBTUwOv14uzZ89qpIUYaGUyGdx000147rnnZIYT1w37tvhvOjqyEP/4j/9YmjjD4TBGR0dx9OhR/OQnP0FZWRlaWlrQ19eHvLw8UdMoKSmBTqcTjbFQKIQ9e/bgP//zP/Haa68tu4dUJriUfelLX4LJZMK3vvUtqV0utaeeego33XQTgsEg/H4/RkZGMDY2hvn5eQ39+9FHH/2NqbcAV9i5qAcHi+Rms1kohnQsKubKQrE6lwG4qFv1gx/8AJ/97Gfl56PRKPr7+zE4OIihoSF0dnbKQCGPxyMiknNzcwLNERIYHh6Wa2NPCtVbGb2RgqvWThKJhEREgUBAJjZSdJJy+GrWQmaXurlXsomJCWGX0JbKdLCDfnR0VAaJ8T6SLPHaa6/BYDCgra1NoDr23FDTjb8DQGRrTpw4gb6+Ptx2220yw0Wn08lBz94bClSmUilRAXA6nfB6vXA6nbj11lvfNCI7efKkaM2lUin4fD709fWht7cX4+PjiMVimJmZ0YiDrtqbm9FoxF//9V/jz/7sz1BdXY2NGzdiy5YtklmqOnbMKriPqqurRZOMe24peYR6cmfOnEEul0MkEsH3vvc9GRlBNuN73/tefPe735U9y68DkD21Y8cOPP/880in05iamoLX60UoFJIaCCnNqoyL0WjERz/6UU2zrcFgwIc//GFs3boVW7duxcDAAJqbm9HY2IiGhgbU19fDYDDIbBVgcV7Rpk2b8JWvfGXFwv5KI5BpFRUV+NSnPoXnnnsOhw8f1tDrVevo6MCRI0dk8F80GpXgF4Do41G1/TfB3lb5l4KCAqxZs0ZTPGcRmvAXMw12eFutVlitVlgsFjz88MO47777EAgE0N/fj/7+frzxxhsYGxvTdO9PTU2hqalJVFfVoUn8mUQiIUV5RmaMqtnBX1paikQigVwuJx3/lOS3Wq0oLS1FKBSSwWMmk0nm0gBaSIswWlVVldwPLnbVCU9MTKCyslIK46SN5nI5EQfMZDLo7+9HNpuVuhM3BIuoHR0daGtrg16vF4YeMxf2MvC98/LyMDs7C71ej+eff15k8rPZrBAWRkZGcPvtt6OgoEAYbMBFmXz2UTz00EO4//77L7npgEVH+/DDD2Pv3r3IZDLSNMoDaGFhAa+88opowq3aW7ddu3bBZDLhpZdeErXjT3ziEzh9+rQoKjPbVCV+XC6XUP35HOgUGLCQkMJue2CRmfbd734XkUhElDecTie2bt2Kxx57TIg5Kq1Yr9dj27ZtCIVCOHLkiAQpzKZZ8yHTzWw2y5ylXbt2afrNzGYz/uEf/gHAIlN0y5Yt+M53viPvxaBRNZ1Oh9///d/H2bNnV2wxUGs2K9knP/lJGI1GfP/73xc4ULWbbroJjz/++IrrNxQKIRaLCb3/qaeeumST+K+bXVHnstIBYzQaRRGVRWD1oCM90e/3C4RltVqRyWTw+OOPY/PmzRgaGhIJe84aCQaDmJ+fRzweh8/nQ39/vxSKuWApy5JKpTAzMyMy9xaLBZnM4ohUdg57vV5MTk7CbDZjZmZGoiWm7ITaYrEYKisrodfrpStYHSlLaGFiYgIlJSXCWGN0yH/zOi0WCxoaGjRDmNi/YjQaJYIcGhpCZWWljHxW9ZUmJyfhdruxbds2GehF6ILMNL4O61jZbBZerxenTp3CBz/4QSEleDwe9PX14ZZbbsHGjRs1VGUAGqbb+fPnMTIygltvvfVNFYo7OzvR2NgIs9mMUCi0TFYHWMxstm3b9kusvt9c+5u/+Rv87d/+rVBjGxoaZB0Di2wsZteErEh6WcpMVP9NOv1S2fpwOIwf/ehH0kibSqWwZcsWnD17VpS71Toj3+M973kPOjo60NXVpVGd4Jmg7hHCc/n5+fj85z8vDuD++++XGs3g4CAOHTqERx55BH19fSKjNDU1teyaKd768MMP46//+q/FAeXn58t8oUtZQ0MDPvaxj2FwcFBql1ScBoB/+qd/QktLi/w87xvvIwVAb7nlFoyOjuL8+fOX8XTfvXZFnculPDKVfQ0Ggybt5SJndD0/Py/6XRx7yh6RQCCA6elpkYVggZKRMLDYFMaFys2jFuDm5uag1+sF+mL0pPaosEitGqEiKgdYrVZMT09LJEhFYgACPY2NjaGmpkYzk4VOhVmKz+fD2NgYGhoaBLYiuYETA/ke4XAYGzZskE3LCDObzaK/v1/GuZKIoDoW1oRYz4pGo5ifn8dzzz0nMykSiQScTify8vLw05/+FJ/97GfR1tam6Xugk+Fo5meffRbve9/7UFRU9KYb9LnnnpOJlGpPgmqnTp16U0mYVbu0mc1m/NM//ZNG/Xjv3r04ePCgDOZikKVCrqrxa6pDYOAXiURgsVjgcrngdDrxoQ99CA888IA4A2bcH/zgB/H888+LgjLZjsyICgoK8P73vx8PP/wwHn30UQQCAU3gpQagwOK+s1qtqKysxH/5L/8F+fn56OzslGyKWfzZs2cxNjaGqakpYcPNzc1p9nFBQYGwQH/wgx/grrvuwujoKKqrq5e1N6xkf/EXfwGj0YhwOIz/+T//J/r7+/H9738fzc3N+OEPf4hYLIapqSl0d3ejv78fExMTmJqa0ohm3nHHHQB+c6CxK8oWYxFv6cMiFs9xp2p0pFoul0MymcTExAQOHjyIvXv3CgsEgNRcePDyNScnJ6VewA3GQ5gHtdlsRjAYhN1ul2g8HA4LFs1CoM1mw8zMzLLPQRn9QCAgdQg2ZlL6hZixXq/H2NgYrrrqKpE+JwSgFvWHh4dRVVUlUT/Vj+kceJ+SySTOnDmD3/qt35ICPTMRvV6Pvr4+rF+/XkPpjsfj4sjIDGImxNc+evSoZohYd3c3BgcH4XQ6cc011whUSfkczrmggzpy5Aj+4i/+Ah6P501FKJ977jl85StfkeeykkUikUv2vqzaz7f29nYZB0G5nqamJrzyyivSQ7VUuLK0tBTz8/NCj+XBrK5RnU6H/fv3Y82aNTCbzfD7/Th79iyampqEKEOpfKvVipaWFnz729/Ghz70IelpUes6VJQ4deoULly4gJqaGtx0002aCaz8Qxq1wWDAfffdh1AohIceeghHjhyBw+FAY2MjgEV5IjJVY7EYrFYrgsGgMBpp5eXlGBgYQC63OFL8tttuw5e//GX80R/90YrUeNUqKyvxiU98Av/n//wftLa2oqysDKWlpchkMvjOd76DO+64A4FAQPZlJBJBfn6+jEYuKyvDrbfeCp1Oh+PHj1/RZ/9OtSuauZSVlUnRXLXCwkKZIsgFrsJjrDGwVhCNRnH06FG0tLQIo0QVcWRUzsN4ampKw0LjQlcLzJxFv2bNGpkJzvcvLCwU4UeOdL1UDSEWi6G6ulrqNsSGLRaLqMNaLBYYjUbU1dVpMhbKpfDP6OgoGhoaJHtT5THUDIzwm8fjkXvADRuLxTA+Po62tjZ5fd4XZhg80AlFkHkWDoexbds2gTaSySSeeuop3HPPPfB6vQiHwygtLUVJSQnsdjtKSkrgdDpRV1eHjo4OXHvttbj66qs1G3ipRSIR9Pf3o7a2FsPDw/D7/fD7/ZqokrNhfh48sWqXtmw2i/Pnz2N0dFSK4wUFBRgfH5c9xP3FA7uiogLT09OaLGOphJHBYMDmzZthMpmQy+VEjSEYDErwYbFYkEwmEQgEsH79elx77bX4zne+g5mZGQ38Biw6q/b2dlm7PT09+OY3v4l//ud/xvj4uPxcSUkJHA4HzGazQM+bNm2C3W7H3//93yOTyUgfDwCZ8RKPx1FcXCxtCXr94pRNj8ejUYVgXelP/uRP8M///M9v6R7/2Z/9Gerq6vD0009jbGwMExMTuO222+D1etHZ2SnBYCgUktk4CwsLOHPmDAYHB+FyuXDDDTfgiSeeEHHRX2e7os6FC2ap6XQ6tLS0aIrDKhuKhUHqYPX390t3N6XmWZgnIYCyMZz7UV5eLgrFbOJiHw3ZaMxUuMl0Oh3sdrvImdAphcNh6PV6acLkgTg5OYnBwUHJVNS6h8FgkEbKgYEB+P1+VFdXS+Sm4rCEB0dHR1FXVyeyLCqThQVOnU6H8fFxUR4gHMbrHR4eRlFREerr68VJq7+vZgp0qLnc4rTIrVu3aoqq4+Pj6Orqwt13341kMiljohlBlpSUoKSkBIWFhThz5sxbEpd89tlnccstt8Dv94seFcc8s6Dc19eHtrY2jVjmql2eeb1eBAIBiZZJ921tbcXp06cBQCCncDisCTwYqKj1FzX4W+pwbrjhBhw4cEDz+6yjZTIZNDc3Y8+ePfjXf/1XPPzwwxgYGBBoNi8vT2j8NMrKEJYuKyvDjh07sGPHDrS3t8NsNsPlcmHXrl2YnZ2VuU25XE6o/v39/QAuZmfUFGxsbBT1ADUI4s/dfffdonjw86yyshLbt2/HD3/4Qwk+b731VgDAT37yE9hsNgn+KEFFUs3Y2Bi8Xi82btyIXC6H73//+8vaHX7d7FcyLAxYjCgY+bLwrWK2aifxwMAArrrqKpkTAkAaBrmIKfDo8/nESVDwkdANAHEsAISVFgwGhY8/MzOj0TZrbGxEXl6ezGGZn5+H3++XDIGd/IFAQPpJAAh5IJFIoKurCw0NDRodMWZmdKzj4+NIJBLweDzi7NQ6inpoDw8Po66uTn6X9ZNcLocLFy5g7dq18h5q42U2mxVnT8fEhsq+vj4hAPC6XnvtNbS0tCAvLw/Dw8MifU/mG7Oq0dFRTE5OviVJ/P/4j/+A2+3WfM1sNsPtdkvntd/vx549e36Z5fUbb36/X9aN0WiUxtqCggJ4PB4ZlQBcVO1mNs1nu1IPkvo11laKi4tx//334+GHHxb1cfbQMFPZvHkzqqqqcOHCBfzwhz/EP/7jP+JrX/saTp48Kew1WkFBAT772c9ix44dcDgcIiHEz0AZG5vNhpaWFiQSCSmKb9q0CS0tLdInpjK/LBYLvvzlL8NsNuOxxx7TfG/Hjh2w2WzYsGEDPB4PFhYW4PP5MD4+LiKbSy0UCuG6665DNBrF66+/DmBxPHJbWxteeeUVOUcItauOOxaLobe3Vzr8Dxw4gImJCdEE/HW0K+5c2PQEQKJhUnr1er10EVPojguWc1ySySTeeOMNbNy4EUajEUajUR4UnVI6nZaJj4FAADqdDtXV1ZJRsHDJB03YitLeAISeHA6HMTExgcnJSUQiEdkcZGyZTCbYbDbE43FMTk4iHA6jqqpKaI96vR52u10kUQoKCtDb2ysRlUq9BiA1kPHxcdTU1EgPi5pVMGVnzYTOxWAwSMGfDiaRSGDTpk0ALnbjMzOig2ERnofKmTNn0NzcrKlxZDKLY4r37NmDrq4uzM7OSrNkKpXCs88+i69+9as4efIknn32WVx99dXwer2aZx8MBjE8PIz+/n6J1F5++WXs27dv2TrJy1ucGlhcXIyzZ89i8+bNV3op/saZqm7MQy2TycDhcGB+fl76udQgpKamRtP/xb6qpbR5Fd7KZrOwWCx44IEH8PDDD0sgZzQaNUHi7bffDoPBgGQyiUgkIuPBX3rpJWFclpWVob6+Hq+//rpA08BiszIA2fu03bt3A1iUs9fpdPj85z+PH/7wh7j55psF3gYWGXJOp1NaAbjGtm3bhueeew7Hjh3DN7/5Tfzd3/0dnn32WZGAmpubg9/vx/Dw8DJWYyqVws6dO2EwGKQZkiyweDyON954Q0OUICReWFgok2rb29ths9lw5MgRpNNpGeH862hX3Lk0NTWho6MDPp8Pr776Knp7e9Hb24uhoSGJ1tk8xTQ5k8lgamoKkUhEMgLKgdOY5hPCYSYzOTmJbDYLq9WKeDwu32Nmw/oGyQCFhYXS18ICPiEtvV4Pp9MpM+4tFotE/IlEAnNzcygsLERlZSXsdjtyuZxIXbCewciekyeXymqQ2tzX16fpgVFNjRqDwSCSySRqa2vldeh0IpEIurq6sGPHDng8HlRVVcHpdMoG48+pKgI6nQ5nz55dNqtmdnYWQ0NDWLNmjQhe8rAoKChAX18fnnjiCfzVX/0VHnvsMZw7dw7//u//LgfT9PQ0pqamBPqLRqP44Q9/iI0bNy5TslZNp9Ph8OHDy6QzVu3yjNDu0kZW1tM8Hg96enoEKmZQp9KR1d9R4VW1IE/nk5eXB7vdjj/+4z/GI488IqPJi4uLJWJvbm7WZAtsrL7vvvuwY8cOrF27Fp/73Ofwe7/3e+jr68Ozzz4rXfXsmtfpdBqtOTqXnp4e1NXVwW63w+FwYPPmzWhpaUF5eTmsVitSqRQGBgakjnfu3Dl87nOfwxtvvCFKEffeey8+9rGP4b/9t/8Gn88nM5ZIZV46ZIwZ4c6dO/HKK6/IHr3nnnuwZ88eRKNRuf98FqxvZbNZcZ579+5FKBRCR0eH1Jh/He2KOxfWHHp7ezXSEryJnGkCXJT5VuW1JyYmsG7dOoGH1AFclOlWO4gJUxUVFWmiftIamZ1wZjwlW9T6BdlkTMPVh83MIpfLYW5uTuaTGwwG2Gw2aVzjJrpw4QKKiorgdrvlWlQpfeLYQ0NDywQyl9JEyTqj9Irar8Lel6qqKmzcuFGmbxqNRk0DpcpQo2zO9PQ01q1bp3F8vb29MtSMjpIzZ+LxOJqamjRsv3PnzuHLX/4y/vzP/1wa7pba888/j5tuukmaVZdaUVERRkZGhG20ar+4VVVVCZFEp9Np+onoROrq6jAwMLAMkq6srJQgDdC2FLBQrq4VzkJiA+X999+Pf/mXf0F/f7+mrlhUVCT0cjqou+66C9lsFnv27MGnP/1pycQffPBBPPLII/jJT34CAJpGSLvdDrfbDYPBgPb2dqxduxYnTpyAzWZDRUUFKioqpPk6m81qPntdXR30ej06OztXvG/XX389Ojs7Nd9nFs8BezRKWl133XUYHx8XWZzS0lJcd911OHLkCFpbW+HxeFBRUQGHwyEySSaTSWqK1157LQDgyJEjck9/He1t+VSqZhEASY2TyaRQi4n5ApA6AsUt161bJ46BgnJkVKnOI5dbFJRcGvXSUZlMJo2czNq1a1FZWQmTyYRwOCwjURcWFgTK47XSGHnodIuzIzZt2iSQWGFhIdLptDQ25ufnY3h4WCiS3Ky8Vm7aubk5hEKhS6o8k5JJXaempiZxhKqEy+DgoMCHav+LSv9kgZf38fTp01i/fr0o0fKg6e7uRktLC3S6i2rMwWAQU1NTWFhYQFVVlYZwkJ+fj+rqavz+7/++jE5WLRaL4fDhw7jhhhvgdDqXUZULCgrgdrtx9OhRXH311W+ymlbtrVh+fj5aWlo0CshOpxMWi0WeTVFRkTSyqnvQbDYjHA7LGlOZXTqdTvpH1BlAudzi9FLqlD344IN46KGHcOrUKSHnFBUV4c4770RhYSEqKirwF3/xF/j+97+Pvr4+qVdyf5L+/vWvfx1jY2PLMjCbzYb6+nq0traKBNLhw4c1P8P+FtWMRiNqa2sxMTGhmQ9FozN48sknNV8nnVg1wu/79+9HVVUVent7kZ+fL5pudHg7d+4UAVej0Yiqqio0NjZKALpjxw7s3r0bPp8Pw8PDGBoawsjICKLRKGKxmMzPebfb2+JcOEaVxkiYC1el3fLwZjGwq6sLLS0tEm3xAFQXPS2bzYrWl2pkoaksJ6vViq6uLlRUVGhmTrAAyjnvpCmrs0zKyspgsVjQ09MDi8WCmZkZRKNRwUsJH1HwccOGDQI/rGTDw8OorKxcUXaCn5mp9MDAAOrr68Vp8D7q9Xr09PSgvr5eqL2E7+jo1LScbKDOzk60tbVp7mcoFMLExASam5vFCVHzaXp6Gn6/X+PUCCf+0z/9k1DAl9qpU6fQ3t6O+vp6mEwm1NfXi9R/RUWFaEAdOXJk1blcIYtEInA6naKMzcxTVXRwuVywWCyyHljMLy4uXibgSIKLz+cTR8Q6jslkQmlpKQoKClBSUiLOo6enBz/60Y9krbpcLlx//fX4yle+gqamJnz605/GSy+9hO7ubnkfrqvbb78dHo8H//AP/3DJniedTifipi+//LLme1TlWGpr1qwBAGHNqVZQUIDbb78dhw8fXlZcX0kpuaCgALt27UJ7ezuCwaCoazgcDtTU1IjAZXt7O/bu3Ytdu3Zh3bp10pYQiURkHPuRI0cQDofR3d2Ns2fP4rnnnsPJkycxPj6OwcFBUXZ/t9rb4lyuvvpqzeJZKvfOiEiv18t0SsJXlMYnZsmUUs1CaKQmq5kLac3EgE0mE4xGo3DcS0pKJGon9Zi1nYWFBZSXl6O8vBw1NTWw2WzCZGtpaUF/fz+ampqEekl9s5mZGYkIZ2Zm0NzcLFnGSs2FLNCvZHSMmUwGkUgEk5OTqK2tlSgIuKjaPDMzg8rKSkxMTAhOTfhQnfhHh8SfY2bFhdvb24uGhgZhyZH5Rjo37zvnuZhMJvzzP/8zXC6XMHuWfs5Dhw5h3759gsGTzOF0OmG1WsXxrmYuV84IN6uFYtb6uBbVYr3KTHS73Zienl6WteRyObS3t+P111+XOmYmk5Hx4KS7k5zzmc98BtXV1fjrv/5rTExMIBqN4p577pE2g/Xr16O5uRnf+973BLIlWaesrAz//b//d/T19eHb3/72JT8npew5LI12qQbdG2+8ERUVFZomawAiI3XddddBr9fjxz/+sUaz781EZ7du3YrOzk6k02mMjIxgfn4e1113HZ577jn4/X4hu3DvGI1GYYam02m0tLQgEAigr69PguRUKiWCskQOSGx4N9rb4lxqampkAVMehTAPI2kAGmpuLpfD8PAwpqam5Pc4KTGXy8Fms2k2BrBYhK6qqhIl3aWy9zabDbW1tSgvL5fCntlsRkVFBZqbm+FwOORQZA0lHo9jdnZWKJcOhwMejwfhcBjDw8O46qqrRHE1lUphbm4Ok5OTGBoawqFDh6RfQ5W0WGos+C+1pQ2Pg4ODwlZjxsf6xMTEBJqammCxWDQNpQsLC0IwYFc96z0XLlxAW1ubMHB4kExPTwsF2WAwyPNghlRYWKjB8//H//gfqKurw7p16wRT5pweYNFpvfDCC7j11lthsVgwPz+/Yv8Te1+WqkKv2i9mvP/qkDgGdGpET4ewVPOutrYWIyMj4ljUIHD9+vUYGhoSjbJIJIJoNLqMVQYs1hTe//73y//ZdMmD9QMf+ABsNhsee+wxYYDa7XaYTCa4XC78wR/8Af7yL/8SJ0+eXPFzbtmyBRaLBWfPntVAXRwyqFo6nUZJSYmocFPBwOfzYXR0FAaDAeXl5bjuuuvw8ssvIxqNori4GJWVlSLzNDw8LGKr6jWcOnUKfr8fs7Oz8Hq9WLt2LY4dOyZZB2G1aDSKwcFBHD9+HH19fZiamoLH44HD4cCrr74qQRwhf1XUdaVa5rvF3hbnwszBYDAgGAyKUikPxsLCQoGROI7YbDZjdnZWnEhxcTEaGhqk8ZJNX2qaODMzg/HxcUmhST1mJMTGQ71eLxIXxHINBgPKysrgdrvhdrtRVlYGk8mEYDAIs9mMgYEBTExMyOJ65plnRBAwGo0KA4vzVILBIF555RW0trZK0V1tRKOFw2FEo1FhfwEXMzp15AAAjIyMoKamRtMjw7/7+vpQUVGBeDwusBYzQLWIT/grlUqhr69P+m9YWwEW5cI3btwoDoeSM7x+vm8kEhE6Z1NTkyZjNJlMaGhogMvlwtjYGFwuFzwej8iOj46OYnR0VHPIdXR04IEHHrhCq27VVM081l7YOMmshYwuQqUMyJgZFxcXyxAxlVjicrmg0+kwNjYm62F2dlYOc9LWCXmvXbsWa9asQVFREQoLC6XQDiwWwP/0T/8UR44cwfDwMOx2u2affvzjH8eePXvw4Q9/GFNTU8sowfn5+VIUf2XJfBS3262Bs2ZnZ2WdEt4bHBzE6dOncfr0aQwPDyOXy+GjH/2oBLrFxcWoqKjA+Pg45ubmRLxzfHwcIyMjyGaz2LJlC0ZHR3H8+HGMjY1hZGREZsycPHkSgUAAIyMjSCQS6Ovrw4ULFzAyMiIjMAKBAFpbW9HR0YF4PK7pKSL5BoCcJe9Ge9toClNTU5ienkZDQ4M0TrIbPJVKafoc9PrFeRPRaBSlpaWw2WwoLi4WWe94PI65ubllBzVTRjoXQkpOpxMlJSWSatfX10tGYLfbNekzD3VgETKbm5tbMdI7deoU1q9fL42S7KUhnu3z+RAMBuW9yJBbaiMjI6ioqNDUmtRuXjqETCaD6elpNDU1aaTQgcWsjrpkhL2YcdEp0GHRsXBzNDU1aWpY09PTIltDzTY1cyF5gky9Bx98UJQQlhrpqQcPHsTNN9+8TEKH1HHaz372M6GWrtovbwzSCOUWFhbK0Da73S6OhWOouU4Ja5Fk4ff7Nf1SRAO2b9+OEydOIBgMSgBCkoDFYhH1hqKiIpGFqa6ulpEbVqsV5eXlKCgoQEVFBe6++258/etf18yQMZlMmJ+fR0VFBXp7e/HNb34TQ0NDy4rx99xzDzZv3qwZBwAsOp7a2lphz1mtVqxZswYFBQXw+Xzw+XxYWFhANBrF17/+dRw6dAg6nQ719fVobGxEb28vXC4XwuGwnAEUzp2cnMTAwAAuXLgg8P2xY8ckA9fpdKiqqsKxY8dEzHZ0dBRDQ0MiwUP1kHQ6jebmZoyNjclnU2nePJNIJno32tviXHK5HNavX49Tp06JI2HNhIuZfRcqvXhubk6EI0dGRtDZ2YlAICCH7VIGRSAQEMFKvq9KLU4mkygpKZH3qaioQGFh4TKhRZ1OJ/pZfr9fo0EEQOT1WQjPZrNSCAUWF/SZM2ewbt06DaV26aLQ6/UYGRkREUAqRbMmxMyFkQsL/3SavG+JRAJjY2PweDxyb/jZn3zySQSDQfkd3pf+/n5UVVVJVMfvDQ8PS8MnyQ1LZWhSqRS8Xq9oSRHTVzua1Wfzox/9CNddd92Ka0NlwqxOnryyptPpUFlZiaamJthsNpSVlcnBbzabYTabUVpaKocu6cJEFOhMGhsb4fV6pYGZ3ffAouT9T3/6UzkICwsLUV9fj927d6O5uRnV1dUCQ6uTYHU6ncDc7FW79957ZRIqHRNhq2uuuQZ5eXn4wQ9+gEwmg9nZWQ0stXPnTpw+fRrHjh1b8T4UFxfDbDaLEy0rK8PQ0BBmZ2c16AFlYxKJBK655hocPnwYRqNRwxTjADBaIBDA6Ogo6uvrJZOjs926dStOnDiBhYUFFBUVYXR0VDIvSkZxr9bW1mLTpk2Ynp6WPUeWHffnu1nM9W1xLqlUChs3bkRnZydisRiKi4vl5tFzqz0gyWQSwWAQgUAAhYWF0j2vNiGqQpO0pUwxRuSq5hEXJCMXi8WCoqIiNDQ0oKqqSphL5eXl0rRI55LNZrGwsIDOzk4cPHgQ27ZtkwmPauNkOBzGq6++ik2bNsnhv7QxjYuFo4pZCyksLIRer5cCPJ3C/Pw8kskk3G63OBwu4omJCTkwVOO1Uj9NrWn19fWhublZXoPF3oGBAaxdu1aD+fL+qfTm3t5ebNiwQYQEBwcHMTc3J4SGkZERJJNJDAwMCKRJOHMprJFOp6WJ9OcNalq1yzOdTofy8nI0NjaisrISHo9HDtp169ahqakJZWVlUo+sqKiQRuKCggKYTCaUl5fDbrdjenpaaqV0LkVFRbjrrrvwxBNPYMOGDWhubkZZWRkKCgpQU1MjumF0IEVFRTIRNZVKiQMzGo0oKSnB/v378b3vfQ/nzp2TwVrZbBalpaXYsWMHzp07J7UXlc22du1amEymS9ZlgItsr1gsBofDoRGLNBgM8Hg84lxyucUxBYcPH5Zrpvjk0noh++A2btwoNG1g8bxwu93Iz8+XYIyBFM8l9U9BQQGamprQ398v7NDCwkKUlJSIQySc/260t8W5EPJibwuLeWrxnhE3YZxUKiUHOzMD1k548KmHNRv32NTI9zUajUgmkxJxLZ0cpxYgi4uLNWwxYDELKS0tRSqVknkMTz75JHbv3i2ORVV31uv1OH36NIqKioQlBkADq7EoGgwGEQ6HUVtbKzUPwgGqoGdeXh7Gx8dRXl4u2Qqdq06ng9frRXV1teZz5XI5qdGQbMDf4WHOSXm8/6lUSjIXlWTBz6V2XY+MjGDr1q0oKSmR8QJLn8fMzAwOHDiAu+66C3Nzc5iampKmTY4xIAT69NNP4/bbb/8FV9iq/TxzOp1oaGhAU1OTzJdnE2BJSQlqamqwbt06bNiwAY2NjdiwYQPa2tpw1VVXwe12o7W1FYFAAEVFRUI55qG4bt06bN++HYcOHVr2vna7Ha2trZL18IAOhUJSG+T+NBqNkr08/PDDywKy/fv3AwD+8z//U1OHABb3+qZNmzAyMnJJfS7WfhcWFrB9+3ZZ/6lUCnq9HnV1dRgeHpaAqqWlBWVlZXjyySdljk0wGEQwGJRzhPsTWKQ49/X1iYoF4cWdO3fi1KlTCAaDMBqN8rkIe/N1jEYjdu7ciZ6eHpSVlaGsrAwej0cgOvVsezfa2+ZcONVwZGREBCV54KrwD//mIB7OISEdV+1JURdeMBhEZWWlMMWYGVAXjIcl4S/+biQSwdjYGAYGBjA0NLSsC/c//uM/cN999yEYDAoEduDAAVx33XXCfKupqYHb7ZbGzqNHj2L37t0rMmf4/1zu4gAx4qjMgHj9rNOkUimMj49rYC/gIvNsbGxsmXPR6XQYGRkRFhrvoU6nw+zsLMrKyiQ74/XNzs6iuroaNptNCq9My9WpmrlcDgMDA9iwYYM4H9XB0iKRCJ566qkVqcWUzykpKYFer191Lr8Cy8/PR0lJCZqbm5fBtU6nE83NzdiwYQP27NmD7du3w+12C8XY5XLhnnvuQXd3N9xuNxoaGrBu3TpcddVVuPrqq/HZz34WBw8exDPPPLPie5eWlkoQU1xcLIEZJ8IycDMajbj77rtx/PhxeL1eaT8AgJaWFjQ2NuLFF1/EwsLCsix369atABZ7qi5lhMKrq6slk2CQxh6tqakprFmzBrW1tbjuuuvwzDPPwGq1ChkiHo9LBkM4MZPJoK6uTvQGGTgZjUZs27YNhw8fFpjZYDBoGr/577y8PJSUlCCbzUrdhgjGr4O9bQX98vJyXHXVVbhw4YI0a6k9HGqtgIfY/Py8RCc6nU40rlZqoJybm4PX6xVCADvRyUzh4UcKMn/f7/djenoaY2NjGBoawsmTJ9Hb26uBbpjm63Q6nDt3DolEAtu3b0cymZReFpPJBLPZLA5qy5Ytizf0/zlLZivsL+Bsjfb2djmw6QDoNBgR5XI5eL1eVFVViTNQpTl8Pt8y5wIs1k9qa2s1kBa/vjS9piBmbW2t1HtIgqAkB9+bE/tIiWbWt7TvKB6Po7+/Hx6PByUlJdKsx/vC2lI2m0VjY+MltdVW7cram8HANBb0dTodSktLBQprbW3F+fPnUVNTg+3bt0sGBADXXXedzLNfapxGyQxdnRfDOmw8Hoff78cdd9yBvLw8PPvss0J2YV1o//79sFqteO6555bVH+hc3gwa0+v1ogWYn58Pl8sFm80m+7OhoQHDw8MiW3XttddidnYWMzMzsj+5hlWoLxQKwWazoa6uDj6fT9ixxcXF2LFjB9ra2hAMBmG1WoVmzftLWDCdTmNhYQEbNmzA0aNHMT09jaGhIQwPD2NwcFD6lt6t9rY5l7y8PLS2tuKFF14Q2jHrBmqDlrrg1K8DFyVfeKCRrqjX6yXjYMGev8eGQQpMEvKi04rFYtJsCCwe1pOTk5pBRXzdiooKHD16FHfeeadGbTmXy0nz56FDh7Bt27ZlGRKLpMwAdDodzp8/j7q6OmHOERZUoxoKTebn52ucC41YMDM2GunQ7BlRIUjqmPE+UlV5cHAQ9fX1mJ+fRyQSkdECxMfpHDlvhfNcuGGWZi4dHR3Yu3evPDur1SpUb7fbLWSAw4cPvyW5/lW7cnYpGFj9PteU0WiULGbjxo0SSCxtUvzQhz6E3t5enDhxQvN1UpLV/Z1Op0WhnHUci8UiWcVtt90mfSYNDQ3SK9be3o6pqSl0dnYuy1z27NmDPXv2XLJ5ksZ5Ljx7iouLZY7TPffcA6fTKdM7q6ur4fP5EI1GMTU1BaPRCJfLJcK2RBusVivMZjNaWlowPj6OsrIyrFu3Di6XC1VVVdi1axd+8pOfID8/X8gNpHqTNMO9v2HDBrz22muIRqPwer04ffo0Tp48iUOHDmFwcBA+n2+ZFM27wd4250KRx0AggLm5OTlA6RyIQQIQeXkasxp26XPkKXn7LIADkCiEDDFGPPX19ZqCN4twKymQUjGZD5DvMz8/j4mJCezfv1+cH5sM5+fnMT4+Dp/Ph507d2oOWrUjmo7F7/fLUDNmWrwnqixOLrcokDk6Oio/q9rk5KTm64wwZ2dn0draKs5JpRSrigAqKWJwcBA1NTUCPRKSUzFik8mECxcu4JprrkF1dTXKyspQU1OzjFGXl5eHY8eO4c4779Rcs0quALBab3kHm8ViQVVVlYzDKC0tRXNzM97//vfjz/7sz5b9vMFgwB/+4R/iH//xHzVfp2SSGvWrhBRq+UWjUaTTaRiNRvzO7/wO6urqcOTIERQUFIicCg//Q4cOLUMvGhsbBd5+M6NTY/2RXyP829fXh7m5OaRSKRiNRtTU1MhQQNZYKZJJMcqqqirY7XZs3boVQ0NDqK+vh91uFwj7tttuw89+9jMkk0n4/X4ZeAhAGo/pYBwOBwoLCxEMBhGNRmVA4cjICE6fPo1QKCSNn+8me1ucC6cY6vV67Nq1C11dXRI183DjA6fIngr/kCvP4jrn3qtii8xc2PxIlkt5efky0TtgEX9mo+BKptPppP5BTPrHP/4xfD4fampqpIlQr9fLYcw0npHR0s3EwWWEz5qbm4VJw0xMZcPx809PT8PpdC7L5IDFRjBK2DO1NxgM0rjI9+bfMzMzoj6gmt/vR0lJiUSl6ufPy8sTVl1xcTH8fj/27duH6upqwd6rqqpgNpul4FtbW4ujR49i3759lxxXTGevqkiv2jvLiouLRWixtrYWNptNRl2TWUXL5XL44Ac/iGeffRZHjx7VDNmqqqqCy+VCaWmpZr7QUhSCbMK1a9fCbrfjpz/9KZqamtDU1ITGxkY4nU5cf/318Pl86OnpWXa9brf7TQ/dhYUFDA4OIhqNSq2T9ad4PA6r1SrUfSqsb9myBWfOnAFwkZjDsRyFhYUIh8Po6+vD6OgoGhsbZcwHcHGNm81mbN26Fc899xyi0ag4KaIeS+uoeXl5MqOK7RkkQy0sLCCXyyEUCmkymGw2q+kbe6fZ2+Jc5ufnEQ6HMTk5KZRkpsMGgwEWiwV2ux0Gg0EaHulgqLzK4nIul5PshUWzgoICLCwsiBoxD+iFhQWZxTA2NqbBLPPy8nD+/PllETdwcSCRChdYLBY89thj+OAHP4hIJCLME77P6Ogouru7ceONN2rgOmYlfE9Sgru6urB+/XqJ4NiPA1zUeGLm4vf7RbKfi4+f0efzobKyUjIjLtiRkREpWqqZw1IdMzrXsbExadAku48ZC+8/e47uuusukdbgZyNho6amBmVlZRgdHUVzc7OMqVVhM/Y4VFRU4Nlnn5XRsKv27rFPf/rT+OpXvyr/Z10wHo/jnnvuwbe+9S3MzMxI34fJZEJ1dTWqqqpQXV2NvLw8qbtms1mZ3TQ/Pw+fz4fZ2VncdNNNOH36NLxeryYIpJbYiy++uOy63G73JQ9Y0vYpLsszJxwOa2B1ylOxNrxv3z50d3eLI7JYLBLs+Xw+zM/PixPg+fPCCy8gEAgIm3JhYQE33XQTHnnkEUQiEfnshNNVdmgqlUJFRQXGxsak0ZI6bpFIBKOjo/D5fAgEAhqtsZmZGRlR8E60t8W5+P1+zM/PY2FhAU1NTTh//rxQ9YDFhx4KhRAIBGRuQjgclma+pU1d/F2dblHEsqSkBPPz88IHB7SZB+sjXq9XPD2phEvpfZScyM/PF3nymZkZfPvb34bP58MNN9wgHc16vV7gowMHDqC5uRlr1qyRehKNr1VYWIhMJiOF7ubmZumWZ3MZswZ+BmBR3aCsrExgBDXaYzGfGRxFICcmJlBdXS11G77W3Nyc3Fe1ibWvrw+NjY1S+2F0BCw6O5PJBLvdjtdeew2NjY0athFZLSo19MCBA9izZw8mJiYwMjKCTCaDyspK1NbWoqGhQSYPvvLKK5dssFy1d661tbVhaGhI9lMkEpHgbcuWLfjGN74hEkTUwyopKUFjYyPcbjcqKytFPJJ7lXuSHfPbtm2DTqfDj3/8Y817ryRUmcstSuKXlJTA6/WuCHdzpMbU1BTi8ThaWlrgcDiEvUU5fpXMk0gkYLPZoNfrUV5ejqqqKhn/EY/HMT8/LygKWWQtLS144YUXMD8/L/doenoaRqMRk5OTmJubg8FgEJkXFbbmvz0eD7xe7zJ1DLXWG4/H4fP55HpJ+nmn2hV3LkwR6Vyo8XXq1Ckkk0k5oNksRemSZDKJ7u5uDA0NSb3FbrdLcdvpdMLpdApUEwwGYbfbZRQxi2XsHeHrU1qBUyg3bdokjWU2mw3l5eUCBfCQDgQCeOihh7B//36EQiF0dXUhEAhI/cjr9eLs2bO48cYbpZ9GxXONRqOIYubn52NgYADr1q2TCXlsvlxKr+b/p6amRMuJDiGXywlThXg2s5ZkMqkhCaiv29fXh8rKSs17pNNpjI2NaaZl0lkxEguHwzAajXjttddw1VVXIZvNoq+vD8ePH8fZs2cxNjaGwcFBTE5OIpfL4dlnn8X69etF2Zo9DZzbwWdQVVX1cwuwq/bOtI9+9KP47ne/CwAaeGb37t0oKCjAgQMHln1Pr9cjlUrBarUKBJ6Xl4fi4mIUFhbKWtXr9Whubsb27dvxox/9SPO+zc3NuOOOOzQkHK/XC6/XC6vViqmpKYyOjoroLbAYwI6PjyMcDkvTr9PpxNzcnNRlnU4nysvLEY1GxaEwKLvlllsALBJlwuGwNHmThkzHqtPpsH79ehw+fBijo6NCuff7/chms3j/+98vwScDRKIL3N96vR6lpaWSldC5qA2XNOo1AovO5Z0s+nrFnUssFhMnAizCME1NTTh27BhyuZwGe+Uhw5ttMBiwsLAgEBHhqPz8fBlLTCprNBoV2iwhJqaUzD6o98WCPaG5tWvXYuvWrVi7di3q6upQX18Po9GIeDyOSCSC8fFxHDx4EPv375f0dGZmRkYav/jii6irq0Nzc7PUXwBoHEE2mxWoqbOzEzabTTOZk+kw/0/j/A0qD6jKtrOzs4hGo5LCs++mv78flZWV4ljonCm4t5SGPDc3J/1A6lRPPh9GduPj46LzlEqlMDk5iYWFBaFjZ7NZzM/PY3JyEt3d3aivr9e8Ty6X08i4P/XUU7jjjjuu8IpbtV+Vvec978GJEyeWFdYtFgt27twpzmWppVIpmEwmydRzuRwmJiZEPsZisSAcDmNqagq7d+/G4cOHNY6CxJ6f/exnorDMw93tdqO8vFzYkuwTOXPmDAYGBjA+Pi5712w2S1BHSFqv12Pz5s0wm80oLy9HU1MT6urqcN999+Hw4cOwWCyiWEAZGo4zYADNTOwrX/kKJiYmMDg4CL/fj0gkgurqao2aBwAUFhbKyGNC6qpKOQM9Mtv4dZIQqBjwG+dc8vLyEIvFYLFYhBWxZcsWnD9/XuOFGWXzQOQNpxNg1zrHCAOLisKcsQ1ACmwUxiOrjBFBLpeTGfR8XVp+fr4UpGl8aD/96U9x8803o7y8HJFIBHNzc1J4nJ6eRjAYxE033SQOlIO51MyJnb2cc79mzRo5xClUyQWlblZChapDYN/K7Ows3G63JuJLp9Pwer2iQaYW81n85z1nKj42Noba2loNDAZAU9w3GAw4efIktmzZIhRMFT5g9gkAhw8f1oxNVo0SMMDi3Ji1a9de3oJatXeM5efnIxAIYHh4eFnt8sYbb8SJEyckM1CNdHyTyYRkMolDhw7hL//yLzE0NCRqElyL11xzDerq6vDCCy9oXmPNmjWIx+OSjdBaW1vh9XqFIDI9PY1z584JPLWwsIDJyUk888wzAp1lMhk0NDRoGIwA5MCfmJhAMBiU9oRIJCIKE8Bi8/HU1JQ0h6fTaeTl5WFgYAD/63/9L+nXo3glAzIGlGwaZz8Z/7hcLhn6ZzQaYbVaReDWZrPB4XAIygAs1lyWTuF9J9kVdy4cDqUKTdbW1qKkpATDw8OivcVCN7ONRCKB4uJiRKNRabKyWCxIJBIiHzI7O4uenh4ZxGMymVBYWAi3262Z/MjUE4CI0On1emzbtu3Nb4Zej9nZWXzrW99Ca2srxsfHRbqG2OgLL7wAo9GIjRs3yrxwKhJQcC6VSok+ms/nE/48swpK4ahFfNr09LTAXsyAgMUN4fP5UFZWJl/jz6hyMHwtvV6PiYkJeDweTXqdy+UwOjoqWK3a8Kmy3RKJBM6dO4f29nZRW15qdBqvvvoqdu7cecn7Sir20omhq/busz179uDw4cMoLi7WOJF9+/Yhl8vhyJEjyzJlyilRW2zTpk0AFnX2VPmjSCSCiooKGI1GPPvss5rX4DTJ3t5eDZ2eHe6E4ghdc5y6wWDAo48+KhL4eXl5cLlcqKysRH19PZxOJ4qLi+F2u5FOp6VoPjs7i0QigYmJCdE0Y0DIwJhFec7PyWaz6Onpwfe//30JRumQ6GwASGmAQSPlY3j+ARCyjsFgEGYm96baGL60VeGdZFf8ytiMxSmNnJDY2tqKY8eOCWxF2q1auCJMo7K8AoGAzJbmz7OOQqXlUCgkWQGL6QCkgYk1naUCikutqKgIP/jBD2C320V4UT34x8bGcP78edx5551yLaq8/VLpfBbO16xZo3EWaiFvabQ/PT0tEx9p7IcJBAKyUdXZL9PT03A4HBoIjUKd1dXVyyjSo6Oj8Hg8moI8NwvrI/n5+VhYWMDWrVuFRcMirKqiACxGcjfffPOK95SqCT/+8Y9x7733vun9X7V3vu3ZswdHjhyBTrc4IK6yshIWiwV1dXXYvn07jh8/vuzAI8LAiaWVlZVwOp3o6+uTDJhM0IWFBaxdu3ZZY6bqXNSBYHRkHAPArnce9q+//jpOnjyJZDKJV199VVoWAAjczrro0NAQjh07hs7OTkSjUZSXl+PQoUOIRCKCCrDnhRm+TqfDzMyMXE8qlUJ/fz9+8IMfaM439Q+zD5XQk0qlUFxcjHg8LrB0IpGQcgGlc0KhEKanpxEIBDQZ3DvR3ha3x8FDxPx1Oh02bdqEs2fPalJgQCuXYrVaUVpailAoJE6DEY8akdP5cHa9w+GAw+GAy+VCdXU1rFarwGTT09OYnp5Gb2+vpqmSmkFqRB4KhfDjH/8Yd955pyxORge5XA7PPPMMtm7dCqfTiWw2K4w2Lg71tZklnD9/XijIKmS1khgnsOgo2K+iOmBgkYXH79GB0QGyuZPGFJxwGd+LkJ3D4ZDPxgXO1+SC7u/vR11dnfwOU3x29BcVFUnktnbt2mWOkowbfq6lTnPV3n22ZcsWkVthDZTqy5/61Kdw8OBBBINBjI6OyuTHYDCIvLw8uN1uNDc3o76+Htu2bUNfX5/0j1AiBQDWr1+Pvr4+je4fRSf7+vpgt9sFmSBSQhZXcXGxBEIzMzN4/PHHJWM4d+6chhXq9XrR19cHi8WCiYkJjI2NCbFodnYWFRUV6OzsFB00Kleoww4BLMvs6WDYK6PWUojcUAqJULo6JoRsWQrHUkCTc5dSqRRmZmY0Ks/vRLviziWTyQgFMJlMyiHKGQ8XLlyQQhaL+2RRGAwGzM7OSoRA2IkPh/BNIpFAQ0MDTCaTfM/pdCKXy8lwpHg8jlgsJvTl2dlZUWmdm5vD4OAgRkZGMDQ0hNHRUSSTSTzxxBNIpVK49dZbNddWUFCArq4uTE5O4sYbb5TsgI2glJZQD3ej0SiaQ6T8vhXjIax24PNzs7DI+5yXl4dAICAwmtoNH4/HMTk5Cbvdvqyuc99992mcOutWVEAuLi6WOhHvN+U6VEpyKpXC2NgYdu/ejeLiYtTV1aG0tFTmhtTV1cFkMqGvr2+11vJrYjwU1WiddttttyEQCODJJ58UpCGRSMjIX+4lq9WKvXv3IhKJYGJiQhiPDKI2bNiAXC6H48ePy2s7HA68973vxfr165Gfn4+amhrY7XaR7s/lctL0yQL6d77zHc2QsYmJCRHOZMc8p2nOz89LrWNubk5D0llYWMD09LTAY6y/cF9NT0/LoLOCggJs2bIFv/d7v4fNmzfLa6jyOSwFcKYTsLifCwsLNVkSe4FmZ2cFslOzFZXO/U60/J//I2/dcrlFnS7O9iDmSPxw48aNOHnyJNavX69JnXnjCWfNzs7iqquukuIWD0z+zuzsLAYHB3HzzTcLlEOGFSdaxuNxGaHMyMZqtWJ0dFQE5GixWAzj4+P42te+hnvvvVdSVzUKeu6553DDDTfIVE0OY5qZmREOOpltZJ6dOHFCGj3fipHGWF5eLgtMLbq/733vk3/TIc/NzQlOTefCKZYs5pMqTZZMQ0ODvA4jqXg8rpGvGBgYkHkVVqsVNpsNiURCHAyZPs8884xcF0dHL7XHHnsMn/nMZy5rLa3aO9f+8A//EJFIZFkx2eFw4KqrrsKBAwewY8cOzfdI3uEB29raCmAR5iLrkqrDDQ0NsNvtePXVVzUadI8++qj8myKUfN+ZmRn09vbC7/fLKONwOAyHwyGQ2czMDA4dOoSCggJhWZENyS566v3RuZhMJsnSCU8vlYIqKSnBjh07sGXLFqTTaXR3d8PpdMpeUiWviJjk5eUJ45Uwc1FRERYWFiQo1uv1iEQisNlsQlgIhUKiWQhcbLF4J9oVzVyYOtIYiRC6WbduHXp6ejR8dUbPTLH5OslkEuXl5XA6neI8GAmx6KXX6zE6OiqccovFgoaGBhFKdDqdkj5TxZRSD7R0Og2/349nnnkGsVgMbW1tGB8flw5cvV6P119/HRUVFbjjjjvgdDplEBPHwhJ7rqmpQXl5uTgXsq2WZg6XymIo+0IKs2rEbpfCaZOTk6irq5NJgyaTCRaLBZOTk6isrNQUP5e+L7Mvg8EgpAf2DvX29qKlpUVYdWyqLCkpkQma2WwW58+fR2lpKcbGxuDz+ZYNVmKQoTL1Vu3dbbfddhs6OjqWfT2VSmHfvn148cUXl61fQlaEeouLi7FmzRqMj4+jtLRURj0UFBTA6XTiqquuwtmzZ3/utSQSCezYsUP6qubm5lBQUIDm5mZ8/vOfx1/8xV9g165dePDBB3HdddcJOYdEm6mpKWkKjsViQgSgk2lubhb5Kg7wWwpXb9myBbfddhvcbjeqq6sxNTWlaTsgxM5gm/uQQ9SIOphMJoH8+XP5+fkIhUKYmpqSKZoqXPhO7hm7os6FH3ppQxKweIg3NjaioKAA/f398n3ijRRJ5M9mMhm43W7RJuLhmsvl5ACjpDtH7ZKNxNGmS6+NvSaqntfExAQmJibw8MMPS7c9BwXlcjlEo1H89Kc/xd133436+nrpMlYdY1FRkdCn5+fnMT8/j7Nnz8JoNKK5uXlZw9SlnIvX60Vpaeklv68SATKZjAhWejweYc5xhvr4+Dhqa2s1WYv6urwWbhDWyaqqquBwOODz+UTv6VLXMz8/j/z8fI3g3ujoqDR5AYu9LXfeeefKC2bV3pVWWFi44swRvV6Pffv2YWJiAl1dXcu+X1ZWhoaGBthsNpjNZjgcDgwODgokpAZVN9xwgwYWu5RRI29gYED6Tjimg530DocDt956Kz796U8L8cRgMGB6eho+n09aDVTSD2uPZrNZBDbZO0bVjYWFBcliVKShvb0dXq9Xs9eZbRCFUR2MGuTxe3q9XgaNsYeH1Gb22eh0Oqkj8+wJh8OagPL/n3ZFnQs/lMVigcvlEtVRwmI6nQ4bNmzAhQsXNEOpWG+xWq0AFimuqoMoLS2V18jPz9ekrIw6eNgC0Ghg0Xw+n/DF+WApBDcxMYGOjg7s2rVLUlJGHI8//ji2bduGm266CQaDQbSD5ubmBL9lSs9FmE6n8cYbb4gUv8rUejMHczlNUXR8AwMDKC0tRSwWExq00WjExMSEFPP5XmrtSiVIECYkPn7mzBmsX78e0WgU8/PzK0ZHuVwOhw8fxp49e5Z9j5kkAJw+fVqw51X79TF1DdHy8/NlANnPfvYzzMzMYGJiAj6fT9Q68vPzRTOrtrYWY2Njcmiyc7+6uhpbt27F2NiYRktrqXEPVFZWykRJtQFRlU+hjhkbkYuLizE5OSlOUg3ACDmR2GIymTA3NyfIiUox5u+qvXVr1qxBb2+v1Ji4v7jXTCaTDCqjmgiL9xThZZBIhRKShjgBNhqNigyU3+8XtQw2cb4TmGRX1LmwIZEsCLfbLXi9yWRCcXExtm7dinPnzmm6yYHFB0kMlfLXPp8PExMTiMViEplToweATJbjuN7BwUFZIB6PRyNEGQqFRDWZ1Eemwk8//bTgpYxGyBo5ffo07r77bpGBmJyclLSYEUVfX5/04czNzWF+fh69vb3ymiv1kawk7fBWncujjz6K//2//zceeeQRaezkBg2FQpidnUUgEJC5GqrxUFAdKGsoJGCcPXtW+mAYUZrNZrlWFiR/9rOfoaamRiIpWjabFXHPmpqay1tEq/ausNbW1hVhK7fbjRtuuEHTdgAs7tXh4WGMjo5KQby2tlb6twoLC2G1WlFeXg6TyYT29nYAEMbVSsb16HK5MDExIV9jvxn3s8FgQDKZFJHMQCCAvr4+TE9Pw+12axwAA12VPblu3TqcO3dOgjciKEv3M62goACJREIcls1mE4dFpCaVSsn7qlqA7JejZAydJdlpRHBIfuJ+Vx097+lSBqvaevCrsMsu6Ksf4tChQ4IRBoNBDA8PY8eOHaiqqkJZWZkMxuHoYr/fj82bN+Mb3/gGBgYGNL0WZJFUVFTA7XaL/AMfEiVieFgTEuOD/oM/+APs379f+lQKCwvR2dmJBx54AE6nEz6fD9/+9rfR0dGBiooKNDU1IRgMwu/349ixY/jUpz617GH88Ic/xO233y6vR5XgqakpWQxUQJ2fn0dhYSGSySROnDiBHTt2oKSkRJMyqwwtQn3skwmHw5ibm9PogF3KioqK4Pf74ff7odfr8ZWvfAUA8L73vU/EIx0Oh2bipRppqiQB9hWVl5cLfburqwu33HILLBaL1MGoakz8lwcH5SjS6TTKyso0m+yxxx7Db//2b1/uElu1d4GtW7cOjz76KNra2jRfLygoQHt7O7761a9KsdtkMiEcDsPv90t/x9zcnDQDdnV1weVyiTQTAJknf+bMGVx//fWXvA6LxYLq6mqNMjIPffZYARczE9YsY7GY1BUZbLI5ksEWdcTIPlUHHQLamrGKBGSzWVRUVGBiYgK1tbWit6gSAqj+bjab5ZpYVojH4xIYMvOis+N+XFhYQDgcvmRmxxqRzWbD9PS0CNMajUY5m99ue8vOZaWC9LXXXqv5mfn5eTz88MN4+OGH8elPfxolJSWorq5GUVERRkZGRLqlqakJw8PDqK+vF9YRHyzhK/ZkAJDO3qKiIkSjUczOzoosDLMMYBEmm5mZEbpue3s7EokERkZGAADPP/88XnzxRZhMJjz22GMoLi7GT3/6U6xZswZWq1Ujb3/mzBmYzWbs3r1bZsrQkabTaRQVFSEcDgszjQs3kUjg5Zdfxm/91m9ppmuqUjFcZGSKZLNZTExMSGFz6f2mqWn3mTNnxPGSG19RUYF0Oo3e3l643e5lLBW+rgod8B4yBXe5XOjv78eXvvQlmM1mKToSSiA75bXXXtN05TOboXp0LpfDwMDAJWe7rNq72wijrmQtLS3y/K+99lpZQ8BF+qxOpxMR07GxMQkeVTmmTZs2CXGAkffCwgKSyaTA6GVlZXC73XIOcDQHM2n2wXBN8hqsVqsc3nx97uHCwkIJ9NTJtl6vFy6XS5wK4TCDwaARotXpdFi7di3eeOMNVFVVIRAICEyvknI4r4pn39TUlPTrkeEKXGw7SKfTCAaDKCkpEcX1ubm5S5JlksmkyPjTEomEKHqsNPfqStpbhsVWgnGWms1mw+/+7u/iT/7kT/C3f/u3KCkpQXl5OTKZjEhP05uy6YlpodFoFO48ayz01tQmYhfv/Py8PJSCggJhj1EuhlZfX48NGzZorrGgoACf+9zn5DOxbqAe5pFIBC+++CLuvvtuqfVQMy0QCGhUn7moWGQ/evQoGhsb4XK5ZBHxYOci4NcIQ3HuxLp16+S6VjJeY319vSbFLSgowN13343Gxkbk5eVheHgYFRUVwmxZyjBj7cpoNArGy+zS6/XK7JpQKISxsTGMjo5ifHwcIyMj6O/vx9jYGA4cOID9+/drrpWLuKysDD/4wQ/wkY985JJrZdXe/RYKhVbssygrK0N9fT1OnjwpihrhcFjqANyjhYWF2LNnjxSmjUajRp2jvb0dJ06cQFdXF06dOoXDhw/j+PHj6O7uRk9PD3p6epBOp7F+/XrceOONcrirjc2UYVHp+/wencJSOSaz2YxgMKhR32hra8Pp06c1jksNzggxq1pgrAUDF2n//Dffk5AYzzOHwyEMWZ6NlIwiRXphYUFGMc/MzGhICAsLC/D7/Zienobf71/x+eRyuTetZV0pe1s69NetW4evfOUr+MIXvoDZ2Vl0dnYKnJVOp2G32wVaooAbJ7EBkPkqZEsAkIVCATimdhaLRaIPSmmr9slPflI8dH5+Pnbs2IFNmzZhfn4enZ2dMi8cuNiw+NJLL0knPhWVSS9kRsWpccw8dDodFhYW8Nprr+G6665bBoPxkFcViLmggUW+PwcSLWV7cFOwPmI2m6XHID8/H5s2bcL27dulcZSaYiQXLGWKqc1cvP5cLge324033ngDmzZtQiKRwNjYmNS/OMsik8kgGAxibm4OTqdT5ouz96empgY2mw3Hjx9f1uuwar9etmXLFpw+fXrZ1202G7Zu3YpTp05henoakUhE4B0GVbTCwkKcO3dOivlcs3Nzc9KVz/EO4+PjMgI4FAphcnISAwMDyM/PR0tLC2ZmZgQF4R5aqe5AeSQyuNRaBnBRFobBGV8jLy8PkUhEnIQ6sI9j2IGLcJnL5UI4HNYgByqhh8oXlMXJZDKwWCxCFuIoaO5TleXKgJqN1FRsZhsHlePZ97PUfhXNl2+b6ll1dTX27duHv/u7v0M4HBadL2L3gUAARUVFovILXPzAbF4EINRCMj8oecCDOJlMSspdWloqI4Bp73//+2XRFBYW4gtf+IJEH88//zx27typKaj5fD4MDQ1h7969Eo2Ew2EpkJWUlGgiEOBipnHs2DGsX79edL6Ai1IvVGZWoww1TSd1mK+l/k24QB0LvXHjRgCLxcx77rlHupzj8bg0VrLmws/BKEzVOeL1EGMeHBzEzp07RZafG0f9LCdPnsTWrVsRi8XkuZaXl6OxsRFFRUV4+eWXcf31179plrtq737bsmULTp06tezrpaWl2LlzJzo7O2XKI5uS1dYDvV6Puro6jI2NieJvMBjEwMAAOjo6kJeXh66uLvT29gpioE50BC4OJiwrK8P58+cRi8UEduOeUDN3nU4nU27z8vJQUlKiaX4mQpJOpxEKhRAKheQ9N2/ejI6ODgkWWTchAqOaTqeTJuule1odMUJnE4lEMDk5iXQ6jampKYRCIcmeVEiNTon6aVarVSB73pPCwkLJfjiYcam91cbuX8beNucyMzOD+vp6vP766+ju7papkHa7HbW1tUgkEsswPx62+fn5CIfDwipRowr2UDBy1+l04lza29vFGbDhyeFwoKWlBQDwl3/5l7IQKM29ZcsWeZ1MJoOf/OQnuP322yUSoQOjrlZ+fr7UMwiXAYv1ppMnT+Kaa67RfBb+W2XGcZETHhsfH5cmRabidCpq1kIHQBFAnU6HT3ziE3LtOt2iiF55ebks9vz8fNEC48JkBsbPkM1mJSrr6enBe97zHlitVlit1mWLMD8/H4cPH8a2bdvEuebn58Pj8YhDfeSRR/CBD3zgl1o/q/bON+oHLjW9Xo/bb78d69atg9frlUI09xODFYfDga1bt4ryOZ0HA6RUKgW3242zZ88KJEQqLvdSJpNBX18fysrK0NPTo5E/Yj2ETb8FBQUoKSlBaWkpHn74YZw9e1akY1ir4d5kYV5FJqxWqwwfY68L/51IJASJYSDndrsxPT2tCbLUDIZ7bmZmBolEAoFAQO4pzw+KW5K2zGAwGo1KMyoDTjZ0c1AhYfhQKCQQPm2pcvXbYVfUuaTTaYTDYUxPT2NwcBDz8/O499578cILLyCbXRwsFY/HUV5eDrfbLdE8AFitVhQXF2PPnj2CLy7tauXhCSziukxtXS4Xtm7dipKSEnR1dWFiYgKTk5MYGhrC5OSkSFa/973vRTQaRSQSweuvvy5Oh3DTyZMnUVpaijVr1mhUjol5MprgdEW1dsHxvUsL2CtJYjMj4KIbGBhAXV2d/F9VS1ahtYKCAtjtduTn5yMej6O+vl4Ke7zGkZERNDY2ygLNZDI4ePAgLly4oMma+Noqs4Z9ADabDZFIRBy0mlaz2aytrQ1VVVXweDxoaGiQQGF6ehotLS0CEazar7etRDzh/uju7haGGA9HZgKk2pIdOTExIU6HfTDJZBI1NTUyqkM9XLkvCJNRsp8NlGqjsU6nQ0lJCZxOJ2w2G2KxGJ566inEYjHpxwMggZ1KhFGdDbCoztzZ2SnvQXko1o0tFosEdlarFZFIRAJQdX+Tbsz7EggEkEwmUVxcvKw+xP/zzGAdOpPJwOv1CoKgKqUDF5XOKXw5PT2Nubk5lJSU/EqINlcsN2KfBxlffr8fNptNpFgIDcViMVy4cAHV1dXQ6XQC9QDAs88+i927dy9TQWadgzd88+bNKCkpQSKRgNVqxcLCAoaHh6HTLc4NcTqdIrA4Pz8Pu92O6667TkYVR6NRnDx5ErfffrtwznO5HLZs2YL29nYZmcxIn125bIICLrKu8vPz0dHRgUAggLvvvlsDh9GWwkO5XE4TEQ0PD6O9vV2TPjOtV7Md4KIU/uzsrMwc52dIp9Po6elBe3u74Ly5XA7Dw8O4+uqrNe+fyWSkoM/PNDExgU2bNsHv9yOZTMrijkajshlOnjyJW265RXoSltrDDz+Mj33sY7/cYlq1d41VVVUt62ci4lBRUSEUYcosEYJyOBwCMZtMJvh8Pg29n5qBNTU16OzslDXLda3OaWLtxGg0IhwOC9wUi8UE6mLAmpeXh3/5l39BIBDAqVOncOHCBemN4WEMXJynomqiEcajPiKdoep84vG47EcGxWpXPq81k8mIwkUqldJom9FxUfWdyALhtLy8PIHABwcHMTExIbNpeB3ZbBazs7OCKnBiLzOrX4Vdkcxlfn4egUBApr7Nzs4Ko4Hz5IeGhkSJtLe3V2h+dBoXLlzA6OgoNm/eLA+LfSzRaBSHDx9GIBBAd3e3zL0nZEOHwsNU1TejsuiGDRswPT0tSr6JRAJOp1PGFGezWZjNZulNYfFRbfbkAc5FSIz4qaeewv333y8pKtP/pd3xap2Ff7MzmcrJKxX7c7mc0I7JfuGGVhUAAGBkZAS1tbWaz59Op5elwXRw1G1zuVw4c+YMWltb5T5RmoKbqKioCKdPn8b999+/YrMns5qlpIpV+/U1t9uN3t5ezddIzLFYLOjs7EQqlcLs7Kzsd3bpk+m0fft2UcYALhJY9Ho9amtrMTo6qqHPA5B9RnHW4uJiNDc3Y2JiQvaoeiADiwjFD3/4Qxw7dgzAIsT+/PPPC1FILfbn5eUJk5JBFutFV199NY4fP64p6LNWw8xJrXFyfzmdTpHOIbJA2RZVVkbN0rhHCe9ZLBYZPMasJj8/H5OTk7JviQixP1AdBU+Y/FdhV8S5ECqam5sT/BKA0BBra2tleqROp4PP50Ntba1ggaFQCM888wzWr18vXbFklvF1n3jiCfz93/89RkdHhbLIwhbF76LRqDRrcZJcJBKBz+dDSUmJkALOnDmDtrY2KWqraTQjIzoclXbIxa0yQx599FFce+21kkkx8uHiWknnR3Uyg4ODmJqakt4c9Wf0er1mxjYdMedBcColHc/MzIxsCL7GyMgI6urqVnxuyWRSYIqioiK89tpraGxsFJqm1WqF0WiEwWCAw+FAfX097Hb7MlVr2iOPPIL3v//9l7t8Vu1dbNXV1RgbG1vxe8FgUIZ+8XCmUgfVLvx+PxoaGtDf349QKCRSMTzgPR6P9MM5HA5UVVWhqqoKFosFHo9HFIMNBgM8Ho8gDOxNUZleXV1dOHDggOYae3p6pL7IiF6v1yMejwsblb1bzEIqKysRiUREx4tfV+ExfuaKigoR02xoaJDvMYNjlqb2svB3GeDSyZCGTCSH1OeCggJks1lMTk4iGo1KUzdRH7LI/H4/ZmZmkEwmV4Qzr7RdEefC5iYaIRQWunhIAhc7R9mgNzMzg76+Phw7dgzt7e3ikVV8lcVrHvRPP/00vvSlL6GjowNGoxGBQEAofHQKsVhMZFGsViuqq6sRi8WQSCRw6tQptLe3ayIh9cBn1sQFzmIgazz8+pkzZ5DNZrFp0yZZ1CQAqJ3xqkzEUuvv70dDQ4Pm/QFIFML3VDOeCxcuwGq1yoJjljM8PIza2lqNUxwaGrqkc1GVAyhdwwyHDrewsBB2ux0GgwEvvfQS3vOe9wiLZelrjY+Pr8q9/IaZx+ORwJHGLvJ4PI5wOIwLFy7AZrOJMjIbIYHF+ozH40FPT4/oj6kNvna7HWVlZcKOslgswjol9M3mY0LkACQ7isfj4ggSiQT27NmDDRs2oKqqCi6XCwMDA4hEIjIHioMHN2zYgJGREZFvsVqtcg7lcjns3bsXr7zyimQZDPAymYxkDQaDAW63G16vF6lUCl6vV3pf4vG45ndU1QzWfmiqNiO7/VlPIvwWi8XkLLDb7XA6nZidndWgOAA0AxzfbrsizoUYIo1pID3n1NSUzEOYmppCU1OTaHUFg0F0dXUhlUqhqalJWCH02myKUg/edDqN2dlZvPjii4jH49KVzwNPxULpya1WK2KxGKampuTA5kNltsIIgpEIWRhc7KojmpycRGdnJ+6++27N1+lk8vLyZLxrXl4eJicn8eKLL2ruG7uYGxsblxUOH3/8cdFVoqMlBbOjowPr16/XQGcAxLmo9+rNMhem+Xq9HkeOHMGuXbtEdYDjE4LBILxeL0ZGRvDNb34THo8Hp06dwrFjxzA5OSnv9dxzz+GWW265/MWzau9qMxqNmr4VYJEFNj09LeSbL33pS8Js4hwTEkZCoRBcLhfGxsakgbGiokLkiIqKitDY2IjR0VEhAfj9fuRyOVGzYMGedRYW8bPZLMbGxkR9o729Hffccw82bdqEz3zmM/jCF76AP/3TP5V1zvESnK45NTUltGXqHjKzYFY/MTEh7RBLEZB4PA6bzYbBwUHMzMxgcnJShqgt7XnjPuI5o+5rtXGT9GpOxEwkEggGg6I3trRuu1I/CwP/t9uuiHOhLL5qKnw0MzODkpISZLNZvPrqq7jqqqs0D+H06dO44YYbBNtcOoGSOCiN8xr+8A//ELFYTJQAiouLJcrmz7E47/f7MTc3h9deew0tLS2yEJY+XF6XTqeThUDclCnq/Pw8vvOd7+D6668X9VL+LnCRaswsSqfTrdhsRqXWxsbGxYfx/5wYG8TWrVunEfZkY9S5c+ewceNGgcwYTSUSCU29JRAIAMCKzBA1a8tms+jo6EBLSwt0Op1AiqFQSCZ3dnR0wOFwIBAISG1lYGBAmrQOHjyIrVu3Xu7SWbVfA1sKsRgMBhw7dkz27OzsLB566CFUVVUhkUiIMjEPSb1eD5/Ph0gkgtnZWYyMjGBubk5EGT0eD4aHh4W6Sxaj3W5HLBbD5OQkzp07J3UdBkipVAq9vb1oamoSh8b9rdfrkUwmYTabNYQYQls8A1aqlRKJ2LVrFw4ePAjgYoMmg2O1gZn1FZ4fKmFp6b1THY+qSUanpg4FJA2c1750suXSVoL8/HzRSfxViFheEediMpnQ2NgohzoLz5RmYcMkG/NU0TSyHbZv366Bc1Rj9gJc7Ej/nd/5HRQVFSGbzSIQCGDz5s2oqKhAdXW1iF+Wl5djdnYWpaWlQoMeHR3FmjVr3rQWwmI9HRMXBbHOb37zm7j55ptRUVGhoQqTckkIj/BYMplEZ2fnMpG/gYEBlJeXC4wALB76Fy5cQFVVlcyIoYPLy8vD0NAQ3G63TNhklDYxMYFwOAyr1SrZT19f3yWzFsJ9AIRQsXfvXqlzcZOwQHns2DFcffXV4nQIBczMzODhhx/G9u3bL2PFrNqvk62E33/3u98VWDudTuPRRx/FgQMHNJMUWS8hlPXVr35VZKLogGKxGOx2O0ZHRzXvx4yayALrq6WlpQAgwSm77EnJpdIGHYCaMdBCoRCSySRcLhdef/11CbjocBiYmc1mVFZWYnBwEMBFx7AU6uJ5oNaDeLjzzKFemhr08vt0LqpYrDoZs6CgAMXFxaKUAUDOBZvNhsrKSjkPTSbTsmD97bIr1udSWFiItWvXoqKiQjxmKBQSwUOdTofXX38d27dvl4M4nU7j6aefxp133rkihVc1OoN9+/bhjjvuEFzS5/OhpqZGFofadAQAHR0dIu+i0+nw4Q9/+JJCb3zQLJ5Tkp/4aX5+Pp588kmsXbsWLS0tGtqf2gXMyINOqa+vT0T2VJuensaaNWs075/JZHDhwgVs2LBBA8fx83d0dKC1tVUDxen1enR3d4uiLO9hX1+f5vV5D8g+IY0zlUqhtLRUnDUzIv6hlL/L5RLnwrneiUQCTzzxBO6+++6ft0RW7dfUKIVE6+rqWjYsLJfL4Utf+hLm5+clWOIaPnnyJADgxRdfxNmzZ4VkQrYixV7ZQKnX6zE9PS17IhwOo6CgAF6vF263WwKkRCIhkDP3aEdHB5qamqRuykwFWBz1QZJBPB5HdXU1BgcH4fP5NPC5injs3r0br776qibDUSH0vLw82Gw2ab5Uf1dV+Egmk5fsDWNdh02bhYWFqKysFBiPo9fVgDk/P18zKkOlH9vt9kvWgK+k/dLvkE6nMTIygt7eXni9XonuWXDq7+8XCGf79u0iBZ+Xl4djx46htrYWdXV1y+oHqrFAv337duzatQvAItMpGAziwoULqKysRDKZxOjoKIaHh0Vji4OvSkpKNKnozytmqV23dC5UO06lUtixY4ek8+SNv5lTPHPmjKaPhe9x9uxZzeGfzWYRDocxMjKC1tZWmaXCVDibzWJgYEDzO1w4PT09WLdunVwHC4hq5qLX62UoEZ8BMeGrr74aPp9PFJ/pfIxGI06dOoXNmzdrOPiJRALhcBg//elP8cADD/xKCoSr9s60mpoaySxyuRy+973vab6v1+vhdDrh8XiE7UR5lWAwiCNHjgBYPES/+tWvSrZM6CqbzaKqqgo+n09EY8PhsMC+zCh48Kp7XR1hQUIP9QiZUcTjcVE2VxmkyWQS+/btw3PPPSfwFDMPNRBtamrChQsXNC0EqoOprKzUkB54sHPv8nOu5FzUDIavSXX5dDotBAhSlFViVU1NjUY5Iy8vD06nUyb2vt32SzdRDgwMyDzqVColfRXERnO5nBSeHA6HPJDZ2VmcPHkSn/rUp37ue/T29qKkpAT79++Xr5FXPjo6KlIkmcyibHUkEhGGydTUFIqLi2UxLWVlLTUuDEZXjJYGBgYwNTWF2267TZOOEjrjgjabzcKGYWTf29uLW265RfO+w8PDKCgogMfj0SzE/v5+uN1ulJWVyWLPZDLIz89HX18fFhYWUFlZKe+ZSqUwOTmJZDKJyspKWfgDAwOorq7WpL/8HjNLUi2PHj2KT3ziEzLXglIx3GCjo6O45557ZJGqWc2rr76KL3zhC5e9blbt18eam5vR19eHxsZGhMNh3HjjjQLbEjLdvXu3MEUXFhak1hEIBDSzWBYWFvDoo4/igQcekK9RjcLn86G4uFgO2oGBASSTSYHFeCYQuloKsXMq6tKvUxGce1oNlNj7NjQ0JIoYCwsL4oR0Oh22bNmC//zP/8TatWs1M5R4nR6PBwcPHkRzc7M4TdZCuM+4z1cyFRa32WyIRqOwWq3CYCVbbN26dRL8US1Fr9ejuLgYTqdT2joCgQD0er2MEnm7AsNfyrlwRDCZEdTIYeZy8OBB7Nq1SwpPvOEFBQV4/fXXceedd8rY4Dc78N944w3s2bNnmbQBsDi98Z577sHCwoJQAAOBAKamppBKpVBeXi4LTU1FL2V0PsRHM5lFOfyXX34ZDzzwgCwe/gx/h6NJbTYbhoeHJd3u7e3F5s2bZdY1raenB2vWrNHQlXO5HM6cOYM1a9bIZgEuQoLHjx/H1q1bZVQzC3THjx9Ha2sr7Ha71EMuBYlxcZMAMTMzg2w2i/LychgMBkQiERQXFyMej8v1k+bJDUixyoMHD+L973//atbyG25NTU146aWXACxqXjmdTuzevRtDQ0MoKyvD1NSUZCwqdJyfn49Tp05pHEEqlcLrr7+O1tZWzbiMsrIydHd3C9We2XMsFkNpaSkmJydhMplkfS/d56z1sOMdgDRNqsQWslRZk8zLy8OePXtk3DnPL543DAxbW1tx/vx5tLW1SVDIwx9YdJp8H1KLeT90usXhh5ymudTUfjcO5ksmkwLvU1Ha6/Uik8lo6tOlpaUIhUI4f/685pw1Go0YGxtDeXk51q1b97bAZL/wK2azWYyOjgqzaWhoCH6/H5FIBIlEAl1dXcsOdh7AHR0dMBgMuPrqq2EwGFBcXCwwzNKDKhAIwOv1orW19eJF6y8qiTISJ2+ckBh1stTGpJ/nWFTjYp+bm8PTTz+N973vfSLyqGK4jJTY20M2BrOkI0eOLCvk53I59PT0aGokudziXOzh4WGZ66Liu9lsFufOncM111yjyUZ0Oh1GRkawYcMGTdfu+Pi4SJarP0sWnM/nQygUwvHjx2GxWGRRsrnLbDbD4XDgjTfewL59+1BQUACbzYbS0lKUlpZKkX/v3r1v+Z6u2q+nNTQ0SFE7mUwiEAhIk19dXZ0M1FM74AljHTt2DLlcTqBfagWySZp7loMAiQjQEYRCIQQCARm2p3bSqwzWc+fOyehkQCvZwkifeyebzaK7uxtGoxF2ux1FRUXYu3evdPPzOtmuYDQa0dLSgtOnT2tQCAZiPN/4bzZs5nI5cTRms1l0Fpcaa8jFxcXLPldxcbE0cMdiMU3tK5VKYWBgAKOjo5iYmMDc3Bz8fj+CwaDAapOTkxqyxJW0y85cCMNcuHABU1NTIrimZgbZbBb9/f249tprNQV2SuZ3dnbiC1/4gmgQqV3yS+306dPYu3evHNjAxXkkMzMzMqdejVh4WHu9XlRWVv7CnO5IJILHH38cd911l6TjwEUclHUZVe6luLgYbrcb8Xgcp06dEkaJauykbWhokNfjgrZarXC73Zp7odfrcfbsWZSVlcFmsyEUCsFisSAWi8Hv9+P8+fO4/fbb5T6MjIyInPhKRmyW82c2b94sDBmTySTF0PHxccTjcTQ0NIgOEqXR/+M//gO33nrr2z7NbtXe+UbYG4CGgWUymeB0OnH8+HEAkD3CArZer8dNN92EXG5x9smBAwfw4IMPauoWZFIB0KAFfD2qTExMTKCtrU0IACyC5+XliQLGSjLzrLuopBm9Xi90Zkos5XI5vPHGG5iZmZEGTsLisVhMFAJGR0dldAYD25KSEtTV1SEQCKCyslLquHq9HiUlJQiHwxqlcvUco7MsLCyUz8XMiPJYvEeFhYWawJMNpCr0Rp21goICxGIxmM1mzM7OakafXCm7rMwlHA6ju7sbr732GsbHxzE3NycwmFoIO3/+PNrb28Vx6PV64VY/8sgj+NSnPqXhc6sZAI0R9pkzZ7Bt2za56WqGoxaxeQNVeM3n8y2b76K+/ptZJpPBI488gltuuUUaqFYy1iXy8vJEir+4uBg1NTU4fPgw9u3bt+x3hoaGNNRtXsuFCxckRV2qHvDGG29g8+bNwtaanp5GOBzGK6+8Iv0pvKddXV3LJnCqKgG8R8lkEj09PWhubpaiImExnU6HV155BTfddJM8Q2rIBYNBnDt3Dtdff/2vhHWyau98o3xRNpuV4nI8HofD4YDf75cDkoENi+htbW3YvHkzPB6PpvbBjIByUDxEVX0/tQk6FovBYrFoWgJ4LvT09GD9+vVyrdxb7F3j19Si+YYNG9DX14eamhohuNx555146aWXJPNSm6yz2Sy2bduGEydOaNAG3ofm5mYMDAxoHGdJSQlMJpMMPlT3PQNylbqs6iCSPccsMT8/H5WVlZqshv2CAKT+S2ICM0B+9rdjeNhbPhmy2Sx8Ph98Ph/m5uY0qa5KseOHYO8Go41kMomHHnoI99xzD8xms2Cm/Bn1MCVLiTUGQjFOp1PTx+H1emUON6Mc1d6M3vdmNR69Xo9Dhw5h165daGhoEIe2kpFtwp4es9kMu92OkZERGAwGEeJUjcrFwEVqcDweR19fH9avX7+M0hgKhQQapAYRHXNHRwc2b96suR5SmWlcrAaDQT4LoTk6X0KMLA76/X5RAiBerNMtCnX+4Ac/wMc//nG4XK5fSTPWqr3zjVMVCf0woLRarVKnUKnENMI5mUxGlNJJJWZ2zTVmsVhEFFOt3xB+UscNs/7p8/nQ0NCg0ejjoa0iEYSoeA4RIWBdhXNgHA4H+vv7pSDO4BpYdLDxeBwLCwvLzouysjIEg0EUFRXBbreLYyHNWr02db+qs5t4fXzPwsJCgf4bGhpQXFy8rM1Cp9MJ2YjOmdpj0WgUmUxGsqIrbW/ZuUQiEZFJiMVi4kWXQk4rFZLT6TQOHz6MpqYmgYLoYHgj9Xq9qHZyoZw+fRo333wzbDabzCDgQ5idnUVZWZlQdZeqA/+iptPpcP78eZhMJmFfLJ2ToBod6tjYGMbGxnD06FGcOXNGGi1JcaRRsI/1EP7+4OAgioqKUF1dLV9nVHXmzBnU1dXBYrHItZDrn06nNXpeo6OjsFgs0kymZj8ABCvW6RZVA9rb20UKh2J3Op0OfX192LFjh4aModfrRdWas89/Fc1Yq/bON05ppMySXq+H1WqFzWaD0+mUuSYUW2SETcYji80ksjCz4cGt0y3Ol19pOJlOp9N8PZPJoLS0FLW1tZienkZbW5uMllDPGP4uC+ZqL0g6nYbb7cYrr7yC+fl5kaC65ZZb8MYbb2gIOjyc8/PzsWXLFnR0dMh+I9SUzWaFVEBFd1Vun4Ef50NZLBY4HA44nU7pVSFKwqypsrISoVBI03xutVoFqi4oKEBhYSFyuZyQgFTJGTWTURvbr5RdVuZC0TUWjpbCUNlsVpomgYsFZL/fj0QigRtuuEFSXRb31RSVGGZBQQEOHz6MHTt2oKKiQuY0qEy0rq4ubNy4UYZlsSBHi8ViMq3yrZpOp8Pk5KT0fTDK4rWpxbqlFovFMDc3J5BRYWEhampqEI1GNY7pwoUL0sRFI3V406ZNKzqxjo4OYaHw+5lMBqdOnUJbW5uGBcL7oqbYXMjM7jhobXp6GuvXrxcBPeKxbrcbJ0+exNVXXy3Pg9Hj0aNH8YEPfEAT0a3aqm3evBmnT5+G3W6XLH52dhbhcBglJSUYHBzUFOMZUbPWqioAq93swMUeD7W2o8K86XQaJpNJ6iyEni5cuIC77roLLpcLNptNivMqZEZnQ7iN75fL5VBRUYHe3l7J9nldO3fuxOHDh+VaqBqu0+mwdu1aYWAyYGZNtrS0FPF4HPn5+TKWgucaD3syx4qKigQCoyoze+sI51FxGVhk6blcLhgMBpSWlsLlcmHNmjXweDxwOBxSt1LPF7J8OSLlSttbdi7FxcVIp9MIBAKCeS6FlijCyAON8E1HRweuv/566Rolo8rlcqGkpETmWJOtlEgkcOLECdx6660yMKegoECYGoWFhejr60NDQ4M0XC29lrGxsctW6F1YWMDBgwdxyy23SMbC91cZIEtN3SCBQAAPP/wwbrnlFlnkKt1xpXpILpdDb2+vaIypRs2ljRs3ymvwdX0+H9ra2mTRp1IpTE1NobW1VTYNF7fJZILVaoXH40FxcTGCwSCCwaBmxIEKi6XTadkAzBYzmQwmJyexa9cu5HK5t6UIuGrvTmOvi3ou0JmsX79eAiBmGYRkaEsDNxUi4to0GAxCBGDAVFRUhKmpKVRWVgoEzqDKbrejubkZpaWl8Hg8ct5Q+p+oB69XJQPx2hYWFhAIBDA6OirqytXV1ejo6BAFD56HzD4KCgowOzsrgRnVoBsbG9HV1SVjRlheIFxVU1ODiYkJCZYBSMalDlXkjKWqqiqhdHO/Op1OzM/PY3JyEpOTk1K6oCoHsyZmaalUCj6fD6+//jr6+voEdrwS9padCz1rLBa75M8kEgmhA9JrHz58GDfccIMccg6HQ/DEPXv2oKmpCXa7HRaLBWazGWazGUNDQ9izZ49085NizDpPJBKBy+VCfn6+ZoaKevCHQqHLGlqVy+Xw7LPPYv/+/Zo52GrBXnUSS2nTXJTPPPMMdu3aBYPBINfGhcq+IEJi/P2pqSkkEgmNM+Sm6ujoQHt7u0QvzN6Gh4eRSCQ0Ug6UCC8tLdU0i/K+MGLLZDI4ffo0Wltb5XtMxwHghRdewO23365xHPn5+ejt7cUdd9wBp9OJ+vr61aFgqybGGsbs7KwIQfIcaG5uht/v12Qo6kGu1j5UXT8142ZQyVlDDPwKCgowNTUFl8sl7DAGVTU1NRL5GwwGkep3uVxy5qi1FrW5kWcKxxiTNMAMxul0IhwOy88z8C0oKMCGDRswMDAgGf/CwgJMJhO2bNmCvr4+KS9wlDMdcnV1tabXhfVlfnaTySTXyCyHiEpRURG8Xi8OHz4swp9+v1+ox8XFxbDZbBLsAheRptnZWfh8PnR3d+P06dMYHBy8IrXUt+xcKD65VP2YlkwmJSpmtH/+/Hls374dtbW1KC0tRWFhofSfzM/Pw2azob29HZs3b0ZbWxs2bNiA2tpanDp1Cvfddx8qKyslAs/lctJn0t3djfr6ek3fCfDmRfo3M9YYdu7cKQrPqlNjx736+ivBY/39/YjFYmhpadE4FV4js5OlLLGenh40NTVppu3RCXR2dmLv3r0ioMms5eTJk9i0aZNsYJ1Oh7Nnz6K9vV3YJCykUkepqKgIiUQCCwsLOH36NDZt2iT3lyQKg8GAgwcP4tZbb0VjYyNsNhtMJhMqKirQ09ODO++8E4WFhb8yCYlVe/dYdXW1wDRqYdlkMkltQF3D3E9c7yqDVB1rzvVJVQqqrXO9JpNJqSnQWc3NzcHn88mY84qKClRWVoo6BaWV6HjUgJLXxOukgCsL49lsFq2trejs7NSMPQcgznR4eFggOOp/VVZWIhqNSqahClLOz8+joKBAVMZJF+Z7m81muFwuEa30er1yD8PhMGKxGHp6ejS9Muyd45iDpTL/ag1HZaHNzMzA5/P90uvhLVMEOB2uqKhI8Dm1w9Tv96OxsRF2u13UfCsqKtDe3o5UKiVenxRjNmE2NTXJlEhqZBHLD4fDEhEcP34c2WwWGzduRG9vL2699dZLOpNkMvmWC83EMmdmZqR5kcZ0VY2u1DRdNc6luf322y/5Xqyr8HX4d29vr6gKq9RIr9cLs9mM2tpaDWaaTqfR29uL/fv3i4NNp9Po6enBTTfdpHF8ZO3QQVPeJZPJCH2Um5eLmaycVCoFm80mTZf19fUiJfF2FABX7d1tmzdvxvDwMGpqaqTpVoWY1HoBcJElCkCkV/hvZkJ6vV4EVS0WC0wmE2pqauDz+TSwGmuAPHCZcfD84DRLdeJrJBKR3yPjk9kPr2MlsoBer4fL5cKLL74oTokoBdEZFtL5mQYGBuRnqA7Pg16Fu8msNRgMgtSQWet0OrFhwwYp2E9NTUGv18Nut0vWGI/HUVRUtAxi5L1U+2f4bHj9POtUwtalkom3Ym85c2EKxjG6KmWO32Pq5vF4EIlE8Ed/9Eeor68XzJ4fiAfk2NiYeE6md1//+tdx2223ycIgbW50dBTf//738Q//8A/o6urCSy+9hOnpaU3UQxsfH0dVVdVb+kx6vR4dHR3YsmWLfE2lOC7NjPhgmEbr9XoEg0E8/fTTuOGGGy556FIHTW2cBBYLej6fb1k3fTa7OGPl2muvlaZGps/nz58XijQzpN7eXng8HhQVFWkcITcD1Y/z8vLQ2dmJXbt2oaSkBFarFcXFxdJFPDQ0hNtuu01kz5PJJKqrq/HKK6/ggQceQF1d3TIpm1VbNQDYvn07uru7JaBR2wDUmSl0HiqTkXAuhRiBi2dOcXExqqqqYLfbYTabhbqcyWQwNDSE6upqOVOoa8im4Hg8jsHBQZw8eRJdXV1So+AYb6vVCrPZjNLSUpSVlcFqtcp1cd48r9nr9aKnp0dYViUlJQgEAnA6nbBYLIICAMCaNWtw6tQpmEwm0VNLJpNwOp2iibYUeqKDZfCtTtZlf1sgEEBTUxNaW1tFT9BkMsm5qtZr+BkIvdlsNpSVlUkNmWdHLBaT4Y2qhNcvC429ZefCqIGFsOLiYomIDQYDKisrkUql4HK5UFpaivr6ejgcDvk5crp5E+PxOAKBAIaHh+U95ubm8LOf/Qx33nmnDP0iJrpjxw75mVAohIMHD+Kb3/zmMqovsIh/kor7ZsYeEoPBIO/HhbXSjV1K6y0oKEA4HMYzzzyDe+65501rEGNjY3C73cuEJMfGxtDS0rKMn55Op3H+/HmsW7dOUzTMZrMyplm9lrNnz6KlpUVzL9SMC1gc6lZSUoLh4WHs27dPUnXOCS8sLMTrr7+OiooKcWaRSAQXLlyATqeD1WpdNnVw1VaNRqow6xt0DNnsonbd2NjYivuKARvrmoSlGVnn5+fDYrFgcHAQGzdulIO0sLBQI6PE4ItDyNjAGIvFEAwGpQmY54rL5ZK5SWqmwL1GfT1mMnl5eQgEAoJorF+/Hq+//jpCoZCmdpPNZrF+/XoMDQ2J0yO64HA4MDExocneaOzYZ92FwSGNkKDX60U6ncbExATy8vJw9OhRJBIJaX5W7zEJC0wI9Hq9kARI96ZTC4fD8Hq9GB0dxcDAAMbHx38pB/OWnQsdRXV1tTgXq9WK8vJyuN1uNDQ0CK89Ly8PV111FQoKCmT8KACNlDWLe3NzcxIdHDhwABs3bkQ4HJZGJmKj69at00xULC0txf33379ik+RSZdMVP/j/yzrOnDmD1tZWjWwFoS+VwcJmLkZcOp0OgUAATz75JO677z4pDl7KVur/ARbT5aXyMHq9Hv39/aioqIDNZtMssFAohEwmg/r6evka+wS4yfga/JusFmYufr8fO3fulICAC4hYL4uNbGI7c+YMamtrJTJctVW7lOn1etTW1qKyshINDQ1oaGhAXl4ePB4PgsGgBo5RnQr3FIOaaDQqEfTCwgLGx8cxNjaG5uZmmbiYTCZRVFQkr0UWFPc21yvV2ql9mEqlUFZWJqwrq9Uq5JV0Oi2wkt/v15BjLBaLhrLrcrkwPDyMWCyGSCSikYSpq6vDxMQEpqenJcvKZDJwu90aFWgiJLwXdXV18Hq9Umti8E6CQzabRW9vL0ZHR0WYlxNtI5GIZD50lHq9XgYSxmIxUXpnkEgUhucDnRNh+V9Gd+wtO5e8vDzU1tZizZo12Lx5M5xOp8AqjICvvfZaxONx+R4AGUHMG8NIhA9/fn4eAwMD6Ovrw2uvvYZdu3ZhYmICoVBIFgIfHF/TbrfjrrvuQkVFxbIDfWFh4efqXdFhBAIBVFVVyWLkIU7GCaOWvLw8HDlyBN/5znfkAfl8Phw7dgz33XefcOzfzPr6+pZBX7lcDv39/Whqalr29TNnzgjNWO1ZOXfuHBobGzV8dY4kID6q1sIY/VAI8I033sCWLVuQy+WkOYwNW52dnVi3bp3QI/laZ8+eRV1dHWKx2CUVD1Zt1YBFhCMSicBut8Pj8ciedzqdGko/AA20zuh5YWFBA3GzFsthWfPz86L8feTIEWzYsEHTzK1Kv3AfMKhNJBLw+/2ih6iyvQoKCgSdSaVS0ltXXl4u2ZTZbJbaDMsAwEWRW1U0Mi8vD6WlpfD7/Zq9ZDabEYlENPeMezk/Px/19fWYm5uTbIl7mE5yZmZGMyiN9RK/3y+SUDqdThTiObuJZwjhQjoXwma8h3xfqtx3d3fLsLTLtctqaWc2UV1djZqaGlRUVMDhcMBmswl2uWvXLkxNTUmEazabUVFRIQ6F0BOLRzMzMzh37hzOnz+PsbExIQyMj48jGAzKQV9UVCSw3H333Sd6WEszlImJiWWZwFLjwltYWEB1dbX8n4uG1D92zLIbfnR0FN/4xjfwyiuvyIwWVcKb92ipBYNBAFjWcMhhR0uZV9FoFENDQ1i3bp00WDHi6+zsRHt7Ow4fPox//dd/xfHjx3H69Ollyss0RkXcyC+99BK2bdsmVE7+XVRUhNdeew1r1qyRuhPvFemhhYWFmi7mVVu1pbZjxw6cOnUKAISxxb4Srh01WCJExfVFVINOh4cn4a6ZmRkEg0GEw2HMzc3BaDSKLqFKtFlaz1EFH1lbUOsTdIL8+WQyKTL+VBFgZs+zgkrFRBN4nQzc1qxZg8HBQQ0Fm9emOkN+Tjo4Ziz8eToCUrFJcXa5XDIAjZ8NWHSUpaWlor48OzsLr9crfTU8E3iv1fox7xWzn1AoBJ/PB7/ff9lr4bIFZXK5HAKBgBz0qkUiEZw7dw7d3d340Y9+BJ1OJ57205/+tNQOKDdgNBoxPz+v+ToZFTzgCLPl5eUhHA7jrrvuwqZNm0QtgFGPen1vRQZGZYUAF6fZUaeI6XEmk5HFCCwyNF599VXce++9mkOf7w1g2TWNjo6uePgza1nqkEhZZqTHBTA1NQWLxSL1oUAggOeee04Wyq5du7BmzRoNb5/XwYzD6/XCZrPJHB5uqmg0Klkfm65YxKysrITZbF42pnnVVm2pbdq0CYcOHRICy9zcnDC2SB9eeqixIG2xWIRKq3bg06nYbDZNkX7t2rWa4Ily9Gy0XPo6RCei0agEdtFoVJis0WhUnAcZrgwe1SZqtWbhdrsxOjqKDRs2CBM2Ho8jmUyiqqoKTzzxBNra2jTEIIfDgUAgALfbLQ6KQbea3ahEJZPJJAEv2WskPPj9fjQ3N8s5mUqlEA6HkZeXJ5kKz0rWo2iEwPjZ6ATJoiOtmyy4ywkuL9u5/Dzcvbu7Gx/60Ic0P3P33XejqalJWE/z8/MAIIci0z42+KhdvtlsFmazGZlMBq2trdi1a5csVv4sb8LlUJCnp6dFtJHvpco/sIANLMr+M/sAFhleTzzxBH7nd35nGXNqaREdWFRBJgVZtYGBAWzdunXZ19va2rB27VqN/EU6ncbp06ellsV+HEY1o6Oj8Hq9+MAHPoC1a9dqWHQUwLtw4QLWrFmDaDSK8fFxWCwWoYdeuHABLS0tQm0mtNjd3Y0bbrgBZWVlGhrnqq3apUyn08Hr9SIcDsNkMiEYDAo0NjY2hqqqKglgCNuwl0MVfWQUrdPpUFxcjPXr18u69nq92LVrl6xX4OJByeCQjkEN9JLJpLDD6DDYB6K2HbBVYunnUqnVlErq7+/H+vXr5bop0EmdNUJ+vBaPxyPOBYDcA76/x+PB+Pg4Nm7cKLVgOiGPxyMN1My8XnjhBbS3t8NkMiEejyOTyWjmviQSCdnPKmOXkKAqh8P7zffV6/XS9BmPxy+rBeGylR7VLvWV7NZbb8VHP/pRzdfuueceubFsoOTDpDMYHx9HeXm5NE4RQgMgXaRFRUWa9FTVH+JrUPjxrZj6OdR5Ciy+sfHrwoUL8tmrq6tx3XXX4VOf+pRIfK9EAKDlcjmMj4/D4/Fovs5oSC3Mq9fFjcGFxd6WjRs3IpVKLZtPUVRUhJtvvhnr1q0TyjgHmLHH5cSJE2hqapLO3fn5eQSDQfh8Ppw6dQrr169HJpOBw+GA1WqVzHTt2rWor6//pTjvq/abY4lEApFIRKBs7tvCwkIEg0GNGGU0GkUwGJRIWZ0Lo3bzE1HIZrOYmZmRMeAqMUAdEmY2m0XoVW0kLigoEMVkBobUDqPMFPcbzyb1QJ6ZmdGMAmCBPpvNIhaLaRCVvLw8VFVVYXJyUtMMyloMzzj25RgMBphMJjQ0NGBsbEy+VlxcLBTsRCKBsrIyUS44cuQI/H4/Hn/8cenXYX2KtR32rwHQaAzyfhQWFooElwqL8TMvHUX9Vu2ynYter78k5ZaH2t/8zd+IlEl9fT1uuukmzM7OinQMKa4zMzPSXdrd3Y3y8nINV5wPOpVK4dy5c1izZo2mK59OgIc6ReF+ngUCgUtSldVCWSaTgd/vR1dXF9atW4ePfOQj+N3f/V3cdtttsjCXMsyWQnJzc3Ow2WzLrmtyclK64nlfl5raazM8PKyZxqk+g6KiIuzfvx87d+6URleHwyHZTTqdxuzsLIaGhlBZWSnpMzWe0uk0/H6/RHQWiwVlZWWoqqqCxWJBXV3dMue4aqt2KWtoaEBfX59A3zxAiU4QElNns+Tl5QkxRs3+1eyb/x4YGJDsnLAuswmVAKMWqAEI84oRv16v1zBSS0pKUF1djZKSEszMzMDlcmn2IJENqlaw058wGlmbJC0ZjUaZ48LzwWAwwO12i1Q+4TUShXigT09PCwuN7RYk5tjtdmHkdXZ2AgBOnTqF8+fPa5qzieawVqM6DNayyUQjjVlV6+BZSzKEit68FfuFNOqLi4sRjUbh9/sxNzcnw2o8Hg90Oh0qKirw3/7bfwOwCIkxrWK/C28UIxOdTidRDiOekpISkXsgHZFCbTQ+rPz8fPh8PtTV1f1cCjIAwVL5GjRGQIxk4vE43njjDdx333144IEHRKIFuOj4VLmWpa8HLEJizE5mZmZw4MABTExMoL+/XyNUqeK6S41Zx+bNm6XXhU2RBoMBd911F6666ioYDAYUFRWhtLRUinP8LOPj46ioqJD7R4FQLjhVU6msrAxmsxnz8/PYu3evzMxZtVV7K2a320Vi3263SzSv0+lENZnIA7C4l6gQwb2kys/n5eVJrSUYDMrBrgZsqrQMzwuypUgRVjMYsqsoMOl0OqXemMvlMDU1hfLycs3nYvZEFisDQ5vNJqww9RrS6TQ2bNiA0dFRyaqoiJxIJARRACBIhdfrlcxB1TVjjTgej2NqakqYc2fPngWwCPc999xzmkyNAXcoFNKcVWTFcZQ5azHMnPj/WCyGUCgkZ8Tl9r1ctnMJBoOYmJiAyWSSRqlMJoPy8nINTfWTn/wkrrrqKtx3332SpgGLB+VS7F6n04nqJ29IeXk5SktLxZvTo6opp0oXXlhYgMvl0hTwVrJwOLxMyZc3na8LLKb2Z8+eRXl5Oerr6yVbogOi1hEZVJdqvhwcHBTnkkgkcPz4cTz00EM4ePAgOjs70dHRIUVMfqalxqKc3W5fRtO89957pZhYWFgojj8QCAj0mEql0NnZKUKVRqNR5kikUinJaHK5HAoLC6WxtKurC3v27HnzBbFqq7bEmFXzrGAfWy6XQ0lJCUKhkOwhlZHJLEaFodRiczab1aiKq2N91a5+1jjYIGgymaSuwT4P9nDNzc2ht7dXRnzzYJ2ampKaCPc2oTO/349kMikQU3NzMwYHBzUsOMq4sHcEgEB27KNZOoeKFGlmeAwkGXiyTuRwOOBwOHDkyBHNBMnBwUEcOXIE5eXl0hjK+hOdGM+qqqoquFwuqbsajUbYbDZNGwefBT9XJBLB2NjYW14Hl1XQz2azGkoaJaYBCKxC0+l0+Ku/+its27YNfX19mtdQG6fU6W9Mw1QeeW1tLV555RV4PJ5lToP/n5ychNPplKIXWWcr9Z4EAgHU1dVp5GjUxcMowev1wmg0iowM8V6m9EylOaWRdEBVjZlOkYvU5XKhsLBQZK3HxsYwMTGBzZs3484779Q4T5W2yGmTKlFgamoKHo9HxjwzwiG2ykiFi9Xr9WL//v2Cc3OjZbNZ9PX14aqrroLFYpGMxmg0YmRk5LLHFqzaquXl5cFqtSIcDkt/FaP6/Px8QTEAyF7nHHn1UOUeZdDFc4LUfe5dnikq7ZhZDw9OMrnIAlX7OTKZDILBoASnwWAQgUBA/q9+Lo5qNhgM0lNSX1+PEydOiD6gigLk5+ejsbER8/Pz2Lp1q/SWkRFGtXNCT+rZyGtkr09eXp7MYgIWe/ruuOMOIUXZ7XY4nU7Mzc1J0K8GvSz06/V6+Hw+QYvsdrucEwx0VT040shJyIrH429p1MZlORdypFcyNuKoRV+32y14Hm8gB/PQI+ZyOQSDQdEV4g2g/D757BSVJOtCZZT5/X5hVjCN5jWp10v4jtfCyIkFRi7obDaLQCCwjD7MB8NJcUx/6WzGx8c1IpSUmuAC5UaiEULcv3+/bD6VzkzWRnd3Nz7+8Y9rnOGZM2dw7bXXAri4yYgl0xFy0QUCAZSVlWkWDO8zYUqPx6OB9tiT81ZgxlVbNdWSySTq6urQ398ve45Bj1or5T4Mh8OS8XOEhOpkGDn39/ejpaVFcwZwT6q1Bv4OkQ2eSZFIRA5hDh5jj8r09DRyuRyKiorg8XhgtVrFOam9MFarFX6/Xzre2fNCpirPn1QqJYSYa6+9FidOnMDevXsFGfB4PPD7/aitrZUglU6SlGPuzby8PFgsFtEApH3+858XheS+vj44nU4NpEYHSwdK0gGdHzXaOK5ZVUbm81HPK2Zqb9W5XHYT5aUWUzgcxvj4OPr7+zEwMCAjeAGgsrJSDis+MKapOp0O09PT0sXP+kswGMTMzAxisRjMZjOKi4uRy+UkCuG1TE1NifPiQ166uPjH5/MtGyPMwp6K9/b29i6bZc8FbjQa4XK5pOOYnfGZTAZvvPGGZp59f3//sq58tZHS6XTiIx/5iDxsNZPjvRgcHBSVWRoL/A0NDRo4T/1b3WxdXV1oaWnR1IcY3dGJE28uKiqS2pbFYrmc5bFqqwZgMRtgZG82mzWafWoAo0Je/FpFRYVGz4vwmMFgwPz8PCoqKjRNfwzs+LOs1wCLmQb3KZEQZud6vV4OYbWuGwgE5GBmfUYlDRQWFmJmZgapVApFRUXyM5WVlZidnZXPazabJbBeu3Yt+vv7RY4FWNz77K9ZKr2SSqVEp40Bp8ViQSgUkvksdNaBQEAjTgto++zU+jadBpEdFvlHRkYQCoUkK+F7ZrNZIRvkcjlxlm+FNAVcZubC9IgRMFlInBESCoVQWFgIu90u7KSamhoUFhaitbUVExMTopvDQ5Z0OY4r1ul0Ik5ZWVmJvr4+tLW1iRfmomVz1oULF3DttdcK3KPSk+m81HrJUlkFFWoyGAySsqsLlA8pPz8fNptNvpfNZmXgj9frlWIlbWhoSNSWaXV1dRgcHITdbsfHPvYxqVOxIYqRFh80e2Ho5HQ6HUZGRrBx40bBVNXPoEJ3zAynpqZwzTXXCNWQdZeysjIsLCzAYrGgpKQEBQUFcDgcqKioQFdXF3bt2nU5y2PVVg2AFhYibVcNdnj4UXZobm5OfpYsMpXSW1xcjEgkgrKyMhQXF0u0nUqlMDs7u4yUw4OaMDQA0UH0+XySWZD+zL1HgtHU1JQoj4RCIQAXHaDD4RBaLqc/5uXlYdOmTejr60NjY6MEzgCkEZuzlNQeurGxMQ2Dls2fZJ1RaoaflcrFrKNwMBsbQ/Py8uBwOLCwsIBwOCzOYWm2qN4jNmuq2aJaKuDPzc/Po7a2VsRI39I6uJxFo9Pp5AHlcovzA1isZyGIM5k5dGZsbAxms1kiarvdLo7FYrHI3HkWmfX6i9MWp6enMTc3h7q6Oik+z83NSQTu9XpRU1Mjqat6c5berJ6eHjQ0NMBoNGrwRy4a3vy+vj5s2rRJHBMdEJVZKQwXCAQ03bgnTpzAvn375F4RklraG2Kz2aDX6/Hggw9qHpJa4ORi5yhil8slP5eXlyeQmIozq7izilmHQiEhXhA6Ay5mkF6vF01NTUin06isrJRg4OzZs7jzzjsvZ3ms2qoBgBSTmUmzkM3D22w2i9Mgps/apV6vl7ER8XhcnNPZs2dx0003yTouKSlBLBbD/Py8BE08OElrVvduLBaDxWJBdXW1zDPKZrPo7OzEmjVrBH3I5XIYHByULnyz2SzXotPpUFZWhq6uLgDQ9Ips2LABhw4dkjOCtF2eNQ6HA319fairq5PCOZvJVbhbRUuARSg9FArBbDZrah6xWAyzs7OiTJ9IJDA3NydBOOs0aglgaX2Z18bPodLAVXIT1aXj8TgqKyvfMlR+2R36FosFBoMB09PTUuBSsxlgsQZCr66mb1Qj5cVZLBYMDw9jw4YNIm+tpm48uIPB4LIifSazOMvh6quvloWiYqN8aOzniMViKCsr04wVXcqpHxoaQm1trfyuCrcxo2BhUH2diYkJOBwOjbNSKciqzczMoLm5eZnOGHBRQ4jZz4kTJ9De3q6BEhjRuN1uea+l8hRqofPMmTMCiakLjin5wMAAbr/9dlgsFtTW1kqkx0a1VVu1X8Q8Hg8aGhowOTkptUYVUeBeZr1FFZ/kfs7Pz0cmk8HU1JQ0W3IceFFRkUjd0wEBEDiLnfGpVEp6OBhosd5C/azy8nI5byKRCKampmA2mxGNRkV9mddlt9slm2HWQCdUWVmJRCIh0BvpyXl5eXC5XOjo6IDVakU2m5XXUfcug1kVymeDJZ0VyRH8ORJwmPkwO1GZu0ScWGPh2cczAoAwynjW8BwhfdpkMsFisUgz7FuxX6jPxWg0oqSkRDq5VQyOmOHU1BQqKiqkABQIBATXozGyJrZPTjY/aH9/P0pLS5FMJiWbyWQW5yxQqVfFD1VjFED64pYtWzSicyxq82eSySRCoZB0v7KI1tjYCJfLJXx59QHwPQcHB0XGhQtlcnJyRYn9YDCI2267bdm10ggJhEIhkemnQ9Dr9TKemAwaOj5Ay8TjZ5icnJTsjj+nbkayRBKJBMbGxjA6OqohS6zaqv0iZjAYpIBdVlaGkpISOdjtdrtg+8xWWPRnYZnSRrFYDEeOHEF7e7s0FQ4PD8tYYIo4spOdGoQMNvl6fr8fMzMzGB8fl4mUr7zyCvbu3avpas9kMpidnZVprAsLC3LQs1+GsBIpzdRCbG1txd///d8L44xZRjQahdPpxPDwMMLhsPxfPeBVlERtMiVRimMwOD1TbdZkmYH6hwCkgRW4KJSp1r1UAtFKmYjqZPg8Gfi/VbvszEVdPDSTySROQy2AM50kpxxYPHTJIltYWJAOcUrCMCXLZDIiYhmJRDROKZlMYmJiAldfffUlD0E+oFgsJhgn4bRkMim4KxfG+fPnsW3bNg0sBVzs1GeUoBb4crkchoaGUF5eLp+XfwKBwLJ+HjJj1Lk0wMUMS32gAwMDMpqVCy+bzaK7uxsf+chHlj18RoWqE4lGoyL8SRHMhYUFJBIJFBYWyqZS61KxWOwXknpYtVVbaoSRmpqa0NHRIQVq1maNRqPAt2ptUy3Qz8zMiNMgMqHT6TA7OyvjtglpE9oiTMa9SxifB6Tf70dHRwc2btwoWU4kEhFyTyKRQGlpqUhAEWZioMxaBuuddGQ2mw1dXV146qmn0NDQIFkEGWDqWWc2m2Xfq7UQdS+ycZKtDqQq08HxHOJwNlKbyTIjQYj7XIXAliI3K7GAecYQiqT6x1u1XyhzARajX/a1kC8NLKals7Oz0h+i1jSIuQYCAQSDQZw9exZut1tuHhdJKpXC6OgoPB6PRB98rXR6cVZ8c3PzMuYJD2E1zbxw4QI2b96saSriA2UE7/f7UVJSIkwtPgCSBNRaBaMkZmzMLtT3vtSY5cnJSYHdaPwdRgVcMJy7ws/MXhWXyyVRGh0Jr1XNYvR6Pc6fP4/29nbo9XqR8iZZQafTIRwOy0wcNUUeHByU3pxVW7Vf1pxOJ9auXYuKigo4nU4ZKcwzIxwOy6FNuizbHjo6OmTqKnDxDGDBn0xSMrf4Nx2UelAT+jpy5AjOnTuHzZs3a/Y2957f7xeCEam9Kku1qKgIgUBAzgo2Lff39yOXy+Hb3/42Tpw4IYe2qoNIZCcWi+Guu+6SPc/aELMXtmUAWj0w/q2WAHhNlHLh+ZdMJhGNRjXkHuBis7YKpa9k6rlCVeTLmef0CzsXADIpkYX68vJyVFdXw+Vyob6+XmQb5ubmxEOGw2FpUmKjEotQXASpVAqhUEhqGKomUTabFd0fOhS1M1YtWM3Pz8Nut0sPDW8ip88RHhsZGcGaNWvkNahxxEKk6gCYhieTSZw8eRKbN2+Wh8UH1tXVhfXr1y+7X8xGlprqJBktUThS/R57aJY2WakUY94jRn21tbXiNJl9sW42Ojoq189MD1hUWN65c+cvszRWbdXEksmkrLuSkhK43W6UlpZKJs89r05EJTqhyryrh3UoFMLk5CSmp6cloiesZTAYhBHJs4EQ26uvvgqfz4cbb7xR5k6ZzWapLUxNTcHpdAK4yHrjwczAzGKxiNIzA+Z0Oo2hoSHkcjlEIhE8+eSTOHPmjObcqq6uRigU0gjzqixQzlXS6RYbRYk2MOviwc7/sybF64jFYiJyCUDYaQBk7zMLUZUBVjLeNwakHo9nxYD5zewXhsV4AW63Wxr0eLjq9XqZWRCJRKDX68UJkVtOJkhRUZEUzsLhsLDQTCaTpmDNm9Td3Y21a9cKfMbr4KGvHrCHDh3C/fffD4PBIAU+4rlFRUXIZrM4f/48Nm3aJDAfHyalXcjvpqzKzMyMpPfhcBjt7e0aNgYpjlardRlkNzY2ht27d8v/1chBjaBOnz6NjRs3an4ml1ucq1BRUSHdvNx0pDpyoTECo3On1hh7WdQMh4w1dUpgJBJZ1p+zaqv2i1g4HMbIyIjshUQigaGhIfT398NsNmvgbuDiXs/lFrWs2traNHVO7m8KOrJ4XVhYKFATD8/5+XnZm5lMBocPH4bH40FbW5vUMzgnhXIx/f39GiQCWNwjJpNJYHur1YpgMLiM2eXz+eRzRCIRPP300wAggrOkNvOQVvt/WM/hyAH+oRFpoWQMWWpUnC4uLobT6ZSewWAwKGKYKtSmBqUqHL9S9sL7aLVaZVz15dgvlbnwQweDQUxNTcHn8+HcuXOoq6uD3W5HWVkZSktLBXLiQW2329HX14drrrlGqHJq2jc+Pr4MliFGSKxVzVC4EFQV1s7OTuzevVtmwfAGMqoJh8PSnVtVVSVenQvVZrNpHk4mk5FiWiaTwbFjx7Bjxw5httExcF7FUsdCzrr6YPm51L+z2UXxOqoQcwGfP38eGzduFKdK1lg2uyjO53a7UVtbK1kaB5FxAbtcLpSXl4sYH1+X7A9VJZoaS6u2ar+MZTIZQS2ARY28gYEBeL1eANrCsppxA8Dw8DCampo0cA6N+1GFugitscGQjYkLCwuYnp7GE088gcrKStTW1grUlUwmEYlEZHBYUVERxsfHJdtnk3RhYaGwYtPpNCwWizgu9WAmtZjXmJ+fj1OnTgkhx+FwYH5+XgLwwsJClJeXa5Sa6QzpPIiuqM2UhBUtFgvKy8vR1NQEi8WCDRs2SKuGWo5gDVgN1lUa8qVgMWZXnEh5ufZLZS7pdBqjo6MappbP50NZWRncbjesVit8Pp+kvoC2Y5QyAn6/X2AlMjVWKtT39PRg7dq18jrARe9Lp8Gi9NTUFK6++moNpZlRDr31kSNHcNddd4nCKQvfAKQRidlQJBLByMgIPB4PvF4vzGYzrFarNIFywY+Ojq44GIzUZNWZqp+DNjg4KJ33qvX29uK+++7T1IQYwTD7UAcCTU1N4e677xaHSehyenoaxcXFmJ6eFgyVVEW+3qq8/qpdCVtYWJD1nclkBMmIRqOa+ieL5VzbnKLodDpFM4+vw6BnKZ2WMDDlToBFeZNz586ho6MDN998s0y6JMuSTga4qHLucDikhlxdXS3FfhKOksmkdMur9uqrrwoMn06nsXv3blx//fVCZc5kMigrK0Nvb6/oifHwZrDIDEvd+9zr6rUBi8okL7/8Mj73uc8hkUhgfHwcBQUFiEajooxACHHpWUn6MrByvYWOUafTiTObmppCXV3dZdVcfinnwt6VpTY/Pw+bzSZ4qbrA0um0QFEcrcubV1xcjOHhYWzcuHHZh2a2odYyGMUzsqBHfvLJJ7Fv3z6hNtJDU9cMAF5//XVJjwnRET7jYiZNN5fL4fDhw3j22Wdx6623IhQK4dZbbxUdL8JRFH6j7phqAwMD0vjIyGSlB3vmzJllVGU6XH4+FvzUoUgABHtWIUXi0MPDw6I+EIvF0NnZiQ0bNkj0V15eDpvNhhdeeAF79+693KWwaqu2zNTCM6Fw9rY4HA7odDphcDHgymazGBwcFKFWIhq0pXuGh7E6RZbw0hNPPIH6+np8+MMf1vSlZbNZEa9kr4nBYMDjjz+Offv2weVywW63w+FwCP2XsDohfjVLYb/Y9ddfj23btmFgYADxeBxWq1WK7XSY/KzU//P7/bKXlyqqq2K5RUVFsNlscLlcMBqN+NrXvoZz587hv/7X/yp0aNZwCN/Rsel0OrjdbszMzMDj8cDpdGJyclLaOlbKDAHImUxyQSQSeXucSywWW0ZDY9ROU8cMRyIRGVZls9mQzWYlJcxms7DZbOLlU6kUSktLMT09LR2xS43SCkuNHxpYZLD19PSI7ANZGirDQqfTaVQD+NCZUajNS6RNZrNZ9Pf3IxwO4/HHH8e1114rDC6Vbnju3DmsW7dOPD8dDIuNDodDrkGNxmjhcHhFut/p06exefNmiewYVRB6JHTHZrO+vj54PB6J+ujIJiYmZHHG43GhRIdCIfT09KC9vV2jtrBqq/bLWHFxsej9cY+wPsjMQW09yOUWdf3WrVsnwdrPs6V1j1gshpdffhkA8J73vEc69VWV4FxusTGRQpDJZFIQlqamJhk/MTMzg4WFBQk+KbNvNpuXNRKazWZUVFSgsLAQa9aswY9+9CO0t7drJluqxftoNKopuKsyTszE1PvD/iCj0YiOjg688MILAIAf//jHuOGGG+B0OhGN/v/a+9LYuM7z6jP7vnI4XIcURVKbZUm2JUuRN8V7HMVOFMeOgyaps9RpXBRofgRNfgRpChQo2gLFV6BIirZuAzcOHKd29jiLLcuO5ViWLFmytUvcOTMczsLZOdv3gz2P3jskJdKWZcm+ByCk2e7ce+fe99nOc56CGPBqtSqf4VqYzWZF7UONFBc6p/X6OXV6tTVjOVhyYp0y8edD84CdarWKYDAoBoMF9K1bt8JqtSKXy2FiYgLJZBKpVApHjhxZ0ICwyE+pbUKl6c7MzCCdTot8S7OnwpOYy+UwPj6OlStXyj7SsITDYQwMDMDn80nUw+3HYjEAc02Oe/bswVNPPYVSqaQxMkNDQ0KfVi94emo0LGoor+LYsWMa4UseI4Xs1PPBfQcg++HxeOBwOHD06FH09/eLp0VvCYB0OjdHnKVSCfF4/G1dRDp0LASOmyCVl14vI3s1YgHmDEMgEBDnhoZjoQWwGbVaDa+//jpeeOEFbNq0Cffcc48QcsiuYkpNXTfIKvvtb3+LT3/60/B6vYjH4xgbGxMFABpFOmsGgwFTU1OaFgiv1ys9dZTFZzRCGXumASkbRaeWaxUd1VqtpulDY2qf5/B73/ueHPdTTz2FYDAo7FkSkeiAUjKG38+GTBqi5vR783lPJBJC+V6ukO2SI5eFdsLtdmtGX5LvTQ8hk8lIAxMZXTMzM/D7/fLefD6PYrEoYpULYTEpFeBc02a1WsXhw4eleWmhC5Lv2bJli3gG9KpYV+nq6sLExIQUy43GubnZLPoBcxHa0aNHMTAwgIGBARgMc7L2fr9ftqWmxYaHh6U4yQtKncSnvm/z5s2a50ZHRzXFdhbhmeJLpVKSv6anyHks/Bc4x22vVqviHRJMT7z66qvYunXrgudZh463g+7ublQqFbjdbrS3t0ttgF67SpXNZrNS71tqJzijHQqt3nTTTbBarSIxQ+exuW7D2qzROCfl39railAohLGxMUkvswGxWZqKUNdEj8cjzFguymwuL5fLmjUmm81qaq9kdarGViUqNRpzzejDw8MYGxvD3r175XtPnDiBl156CVu2bMHMzIyme5/M0Xw+L4ZteHhYhHEXM9pqawcHnnV3d8/TSbwQlmxcuJipnm1LS4scTCwWw8jICMrlMiwWi4ha8rMulwu///3vcd1114kgJT8LAC+++CK2bt0qj1XEYrHzKvRywU0mkwv2lwBzF+vevXuxefNmDaWO5AKz2YxUKqWpudC7OnDggMiId3R0YM2aNdi8ebOEjAbD3ECvbdu2LZi/HB0dxR133KG5WLjfRDwe1+iuEUePHsUNN9wAAJowVvVGSFhgxEWaNf9I0yQnf3x8XEgHBoNBJPfj8bhezNdxUeFyuZDJZOS6WrduHUZHRxGLxaQ+wmmobW1tQqFvrrecPHkSv/nNbyTFy7aCfD6PlStX4itf+YqMv2DtlIKYqlAtF3pmF7LZLPbu3YuHH35Y7n9unwZApUerFGXg3D1MkgyjI3USJmsW7KnJ5XLwer2ynvJepkFQwWimXp9Ta37zzTfxwAMPwGw2Sy3n7Nmz6OjoAADph2GajT1GTK2l02kxXjwHKmhUqaBA9mmz2shSsGTjEggEMDExgc7OznMfNpsRCoVw5MgRYY1RTiGdTgsbgiqh2WxWmBfqfGjKQSxU5Ca190I4ePCgLJjNJ4zU4c2bN2sYVepizyJ5NpuVH4MX1f79+zEwMIAdO3YgEolIc6caPqpTOVXwB2bUQPHI5uM8fPjwPJYZ+fwej0f2lQaGho0duCaTSeotV199NZxOp9CMKX3DPLHD4YDD4RD2HKnHHHmqQ8dSoDbeLga3241Tp07BbDZjdHRUI67IdFMmkxHGItPYqhiuwWAQCrE6CRcANm7ciM985jPSmU7ZKE6RNZvNGBoaQqFQQC6X0zjH0WgUTz/9NL761a9KcRs4J+LIe5SFde4LU05qncTj8WBiYkLqzm63G8lkUrQR6dCy14X3JHCO6MR0OZ9TU+t0dm+66SZYLBZcc8016Ovrg9VqxYkTJ/Daa6/J/rEGy+iM6S+1B07dNrfPz7Otg0wx7s+72ufSXGDLZDIYGxuTBdlsNmNmZkaK4Coz4oUXXsD69es1HHQe5MGDB7F+/foF2VMTExMy4GsxsCjPJioVCxkWOXilC5W0O+YVmco7ePAg/vRP/xSf+9znxMipNQyj0Yjjx49jxYoVC+5bLpcT9pYaEfHm4nelUql5NaWxsTHNcDP1omBultprsVgM+Xwer7/+Onp6eqRfh13GLpcLXq8XhUIBt9xyC/x+P/x+P4LBIILBIA4cOICdO3ee9zzr0KFiIaZoM3K5nKikA5C1AdBmHNix3xwh8B612Wy49tprNcasra0NDz30kBB4TCaTyNmztuH1etHV1aWZz+R0OpFMJvHMM8/g05/+NIxGo7RLqDJUNDJUBWbdgSMDvF6vOGrBYFAW8XK5jEAgIIwtknsajQZ8Pp/0/qgaYQBkLWrObPCYmWKjunEqlQIw15fGjn7OW2EaT60JA+cMmWq4+R0833Q46YyqQrfLwdumImezWRw7dkxykrSS9Ji548w/qrlA4BylkF7LQtPNRkZG5oVjaoMQcfjwYWzdunVesfxChoWSD+S98zvZYPXGG2+gpaUFXV1d0g3cHK4bDAZMTk7i5ptvXjCHOTw8PM/wNBf0F9MiY0qMFxf3tVarwel0aqTK2RQ6PT0t3hPZYCySOhwOHDt2DI888giq1apIWDC6eTuhr44PLuhtL5aLZ4M17y0ylhhd1Ot1RKNRdHR0iMIHnVGuBzRApOOyO97j8eBP/uRP0NXVJd3zfH+1WpVIJp/PS4qK+zw+Po6nn34aDzzwANxut9ReVGULCtsyLcYufY4k5jgKKltQ1p/norOzU3pTzGazkGi8Xi+GhoaEsaYal+Z6Dl9T+1MMhjmZmEQiIWsadRM5dVNtdeCxLKR2wrWMEQn7/NRx7gaDAT09PQvW3C+Et2VcJicnMTU1JfnMcrmMTCaj0cpR8dJLL+Gqq64S660uwkeOHJGZ8804deoUPvzhD2uea6bPpVIptLa2Sp+LGloaDAZs2bJFLgCm6VjkpoHhSVVnTLz44osIh8Po7++Xm4hQi27pdFpk+hfKYSYSiXn1ombu/vDwsEacj++xWq1obW3VfH+j0RAaNUc684KZnZ0VSQ3eYJxkx8+oOmhUQdBnt+h4O3C5XDKJ1ul0ijOTSCQQDAaRTCbFQeQURQBSL0mlUqIpSMewWVzW4XAgFovhwIEDuOaaa+B2u/HjH/8Y119/PdavX49arYZEIqFprmT/CgUuyU4rl8s4dOgQjhw5gvvvv18kk5ojJbXPg9tjZKTWSNhDwnsKAFpbW2Gz2aS2sWLFChw9elTeR+UP7ivBdYvfrSpD0+hwVEE4HEapVMLU1JSww1wul4wvYQ1V3T8yz4BzQpiqbJXJZEIwGJT0mdvtRiAQQEdHx7I1xYhlGRdVNoUngQvVzMyMNE2yqE+vI5vNajroiWQyKZ5KMyYnJ0UxmVBDOEYb+/fvx4033igd9uq+qVLzDIcph0JrzlSay+VCPp/HyMgIXn/9ddx4442SGuN3qnpefO2tt97CXXfdJcJxah8LANEy4/6rnyd4A6jHyYYlCsyxVsNCGz0xemqMEFtaWsSA53I5tLS0IJFIwGw2y4XfjBdffBEf/ehHl3Mp6NABAJKTT6fTOHv2LCqVCkZHR8WQ5HI5+P1+TExMYGxsTLQFOfaB6wSvYzp87CsZGxuD2+3G7bffjkajgVAohFQqhV27dkm3PXBucWbdlwSkYrGIYrGIiYkJPPnkkwgEAvjoRz+qEYFU7zE1O8D7jY4rCQD5fB5er1ecaQAivskemL6+Pvzxj38UtqxKKmjWDQOgSRUC0LzO9Wx4eFgIS3TmmWIjs4uSNmazGR0dHSiXyxqigSorw+PkOadY7sqVK2Ui7TtpTViWcTGbzRgZGYHZbBa6H61co9GQCWwsjvt8Pjz99NMibd0c0bz22muLdoMfPXoUO3bskMfq50nnTSaT6OjoQDgcllSWerHyczQEpPvRQ+JrNCwvvfQSbDYb7r77bqH/kb1G685tc3v0WlSZhcUofmpUxQuCLDR1Xw0GA4aHhxGJRESTTDUwqoGipwHMFSjXr1+vCecZ8qdSKezbtw99fX3zostKpbJsmqEOHSpYw1OdHmBuQcxkMshmswgEAiLVxMVdTXEz+labfik5UqlUpFn6rrvukjSYyWQS8UY6tlNTU6KHNT4+jt27d6NYLOJzn/ucNEjSWKj3E0G2VCKR0NyDdrsd2WwWIyMj4rRyHhLv7VAoJGmweDyOdDqNfD6v6cpX17LmTE4zuCbk83lYrVbMzMyIGC3HQWezWWSzWQSDQY1hiMViogY/Ozsr76MhYXRDp7mnpwd9fX0XbdzGsoxLIBDAyMiIFJ4DgQCmp6dhMs1NbeSkuUgkIl7EzMwMtmzZgunpac1JpOVV2VtEPB5HKBTSRA20rgzlOEvl3nvvldCRkQEbpnjhMrfJRZgprEKhgAMHDojHsWbNGhk+RDYIAJnpwn3kD37y5EmsXr1aBgrZbDa56Pm9aq1HDXu5vZGREfT09ADQileOjo6Kt6bqorlcLqmNVKtVyWEDc42Q7EimESXHfWpqCocOHcLKlSsRi8XkAmKDmw4dFwNUCwbOOWTsJwPONTDy/0xDqU5bNBpFKBTCtddeCwCaoX6s7xoM57rGOdvon//5n3Hy5EnxyJnmttvt+Na3vqWpo3AbrLOQTWk2m+Hz+RCPx7F//34MDg6KEZmZmUE+nxcNs0KhIMxSjulwOp3o6+vD2NgYotGodOLzvlcL66qzuRh4XihRwzk2bDEgC87pdOLEiRO45pprZIont820JBXReb7p2NrtdgwODkoQcLGwLOOizhcB5lgE4XAYhUJBQtlgMAiTyYRcLoc9e/agp6dHQjC1h2Xfvn2aWgQvRIvFgrfeegs7duyQKKhWq2mG4DBqYa2D1EMW3thZS9BC1+tzE+TOnDkjdMBVq1YJu8JqtUq0wgufNRnmWlWmWCqVwrp16+RipdFjHjcajYrcggr1OMbHx6WPRaVGM0RXDSw9PlIQR0ZGNN4XGWTcFr0l5lVJ/R4aGkJbWxsMBgP27NmDm2++eTmXgQ4di0JllKr1QIPBoHGEVGctlUoJdddgMKC/vx+RSAROpxOpVEpGBav3pYpsNgun04k77rgDp0+f1qiJOBwOfPnLX4bL5ZoXVan7qbJNrVYrfvazn+Hzn/+8hgFGI8dJknSggbn+kra2NgwNDQGYS4dnMhnpm6GBovQVm8nV87CYoZmZmUEgEJD3sX+GuoxU6GB7B7XQgDnDQimXSqUimmlMoxkMBkQikYtuWIBlGheDwSCzTTi2mIJ0sVgMa9asQblcRjKZxPT0NHbv3o0777xTpO35o+ZyOalzqOCC7Pf7hQrHvKm62NfrdQwNDeGWW26Ri5eGiHlQji2lQTl16hSq1SqcTifWrFmjUSDl+1i7aJZpsdvtctPweV7QvHFUj4iLOc9J8zGqnh1rMirFOJ/PayI65oHVEczBYBC5XE68sXw+L6+R5kiDybCcQnx2ux35fF4UFnSWmI6LBY4vB+YcomKxiEwmg1wup7lHCLPZjGAwiFQqhc7OTrS0tKC9vR2dnZ1S36DDpw4T433L9LTZbJaU0NGjRwHM3TuDg4PC9lxIr0ydicQ1hx3vZL6SuMT2iWw2i87OTk0mhIt9Op3GqVOn5H5sbsQEgPb2dqRSKQQCAc3r6ppDZLNZYaKptWKTySSsOa4/2WxWBDU5zZO9KoygJicnEQqF4Ha7ZZpwOBy+6IYFeBtsMaPRiK6uLmQyGQwNDSGfz0taJZ/PY2JiApVKBRMTE3A6neItqDWJyclJbNmyZcHtv/rqq/j0pz8Nm82mMRD0EIzGuQmTTJuZzWb4/X4ZskNDk8vlkEwmMTY2Bo/Hg/Xr1wvDitGQyi6j0WL+kSdbDcn5mXp9TiPt6quv1qixqj8QyQzn0+NhxKWmw4C5C2r16tWSMuBMGqPRKJ30HKzGmRO8udQpfLzogHM3Gr0bflfz2GUdOt4J/H4/0um03FulUgkzMzOakRtcaFlLNJvNUifgKAt21zNiUKP65rqnwWDAzMwMfvOb36C1tRXj4+OYmZmB2+3Gvffeq5lkq4KLP5HJZHDs2DGcPXsWn/jEJ6S2QQkU3jeVSkWjIcjjoiNMmjWAeeK2FosFkUgEhw4dEu0xNZOhMrh4nsieoyMJQNZVpuR5zgmuZ4lEQtauX/ziF/jd736H66+/XthoarPqxcbboiLzZNAbYWNkOp1GOp1GLpfDa6+9hp6eHvkB1ANYbMrh+Pi4zBjhD0UvRbXoJ0+exK233iqdsKTWWSwW5HI5pFIpDA8Pw+l04sMf/rCwyFReuaqQSq0vGh7+wKoxaf7BVfIAoM2hqkyx83kE6XR6QTFOGk6eN9ac6FmxsG8wGBAMBiWKuv7661Gr1aR5jAOC1D4Z7jML+Po4Yx0XExaLBd3d3YjH48jlckLbVRmUqqHgdR0IBCT97XQ6EYvFhHWmdsw3Gxlgbh166aWXcM0116C7uxtdXV148sknceutt4rz1lw4Zy8IDUytVsMvf/lL5PN5PPTQQ+IMNxoNkVVi1MCIQI2CeD+p/TThcFjkZrhG0fllipsGhWsPzw9VTbxer2yP6xEzOEy9tbW1IR6Pixgv1yf2/4yMjGBmZgY//OEPEYvFcPr0aWl94OyXdwPLNi4TExNYvXo1zpw5oynSUbaaubxYLIa1a9dqPPsL4cCBA7jnnnukW1ZNpXGRjcViwvfu7++Xi8fr9SIWi2F6ehojIyMIhUKiNVSpVOQHASCPgXNKpQybaQyaIxKV5bVQLWUpx9eMdDoNv98vj9UbkLUU1nACgQAikYjsC2nVAMS7o1fFAieLjGqER078UuTMdeh4O3A4HOjt7UU2m8Xhw4clDducEjYajXC73WhtbRWnMp/PY3p6GslkUqSN1AZA4Fw3OdmQu3fvxh133CF1hXXr1uG+++4TQgA/Ry+ddUh695lMBj/60Y+wY8cObNmyRdLIJNw0Gg2NkWQNVu3b4/9JXmiWrzEYDJqZTCqpgfeu+l71u9TMhro+sfeGkcmqVasAQNoxGo2G1F5+//vfi7L7T3/6U1x11VVCWFqoFeRiYNlb5Y+vIp1OS8c4rSawvAV3cnJSCk1kZzWjXq/j7Nmz0o1/5swZEVVjusfpdKK/v18utGw2C4fDIc2FXq9XFmNeuAxjeaEsxuLgDTE8PIzrr79+wX1cKG+6GNLpNDo7OzWNofw/oxW73Y7W1lbxQvx+v3gzHo8HyWRSvpeRHBlvbKpkTYXDfxjt6dDxboIq3RyVuxB4jbOlgdMU1foIHSyLxaIZZ0HS0O233y51Sy7AmzZt0qS96PGrlPt8Po8XXngB2WwWX/jCF7BixQohA9GwORwOkWThd/b29kq6ivUPZgTUlBgJCewFqtfr0uFP8POqY1mv15FKpeD1ejU1XJ4vAJqI6/Tp0xgYGBARW+o68lzUajX86le/ku98+eWXcfbsWaxevRoej2febJqLhbe1wnCgVCaTQaPRwMjIiBTGKpWKjANeDv7whz/gYx/7GID5cxz4bzQaFeVUUhKpaqyqBPOk0sBwkQ0Gg+jq6kIsFpPOYs5gWKjwRqg/LFNdzUoBzaH6Ugpk+XxeSA2q2itBlh2l/KmEQG0js9ksERRlwWlkbTabiFN2dHRo9oe6Yzp0vJswGo3o7e2VJl4W4Qm1VwSADPBKJpPCiOJ8eV6/3E42m8XLL7+Mj3zkIxo6s1r0VtmTwLkFuVKp4LnnnsOJEydw66234o477pDvZ+GbqWe1z6zRaGB4eBiDg4PiTPN4qtUqQqGQxuhQR4wFeaqAUP+LdWLum0rqUdcvHjezEMxosMFzfHwcH/rQhzA+Po5GoyHGa3Z2Fi6XCz/4wQ80kzNnZ2fx3HPPyWTcd2s44JKNy6OPPgqHw4GJiQkMDw+jpaUFnZ2dmJqawvHjx9HT0yML/vDw8LLmggwPD2u6x5uL40QsFpNcIb2E5tQZf1i1EGaxWOD3+xGJRGC1WmVCJnnrai1F9SAArZGo1+s4duwY1q5dq6EPNn+G+7cUqNtRDRNvEGqAkQrNi49CdcA5mQo1lVcsFmVqHo0NMJcbVpWtdeh4N7F27VqMjIyIDh4XUy6MvHfUTvHm+oPqeDErsH//ftx5552a+52NxnzcXCyvVqv44x//iIMHD+KTn/wkvvrVr8rrZHoxagLOSeCzydhkMuHUqVO45pprZB+ZmmLPi9oWwOm6NBaMukwmEwKBAMbGxtDZ2alJg/E4+JjPqT1B7LBnU6fBYBARS5V9ynXhi1/8Inbt2iWjToLBoPS70Hl9N7Bk4/Lf//3fYhGfeOIJuN1ufOtb30IkEkE0GsXAwIDmx1SlES6EP/7xj7j33ns1zzV/dnx8XKZcqjpC6rwDhq8qbZcF8nXr1kl04/F4pPDGWQvqQJ9m46Iaj1wuh/b2dmGQLLTPDEXP15yo9q+oaTQ1lFepyWazGTabDV6vFyaTSSZzkhpOxpy6DzabDaFQSJo0LRaLNFnq0HEp4HK5cM0118DlcknnPFOyRqNRZrQz0i4UClLsZvqaxXUamrNnz2LH/6l3qKkw3kvqPUvD9Morr2B8fBy33norPvOZz8h91NHRIXOoaEzU+ipRr9dx8uRJDA4OSu2W9VwyM2kYuKh7PB6cPHlSatLqOuLz+TA0NCSinaokTDqdhsvlmmcwgXNyW9yHEydOYPv27XLsnOXEqC2TycDlcqGrqwt9fX1SfggEAlLves9rLps3b8YLL7wgj3fs2IEVK1YI/5w0ORbjl2pYhoaG0N7efsE0TTwelzny5XIZv/71r7Fx40b09fWhUCigvb1dpPenp6fhdDoBzF3cbW1tqFQqkiZqNBoIBAI4fvw4crmcKKc2UwvVfKfBYBDlAFp8leZLqMapue6ihu8WiwU33XSTJrzmRUHPRy3WAXM0T7fbLSOTbTYb+vv7cfLkSamjkMFHz4Sf06HjvUJnZycqlQoikQgqlQqi0ShisZj0jkxPT0uanSluXvuMEHgP0qki5Z73xujoKJ5++mlJmwFz9x977L72ta/h0UcflbosmZdmsxnd3d04duyYxsFTMxmk9D7xxBP427/9WxldwVQXF3W1fsT0l1qQp3PL+5zrAcHSgtfrhc1m06QQ1X2ZnZ1FIBDAmTNnsGnTJnR3d2vqOEajUYaRca0hvbtUKmH9+vULDia82Fiycdm1a5cYl4GBAfzN3/yN9LTQqy4UCovKxy+GV199dV7U0ozh4WHxvvlDHjt2DPv370dnZye2bduGG2+8Ed3d3SgUCkLXdblcCAaDMk+lWCyio6MDXq9XZLPZzUoNLjZmqWEo+2NOnTqFrVu3apgvatgNnJN3UOcyEKrh4v6pysbqZ+hNsPOWsyRoKNQiIy+mRCIh0SPZdssd8KNDx8WGzWZDR0cHYrGYpIRSqZQ4aAaDAePj47KWOBwO6Yz3eDya9Hcmk8Hq1avldTp/zCZEo1HNdwcCAfzd3/0dWltbZYy62pxNwV3WN9X7mcbm6NGj+MlPfiLRSHNvGu95ZmtIaCIhgO8Dzq0BaosGoy+uOZway9eYLqTD6fF4kM1m0dbWhquuukoivmaqdTAYRCgUwooVK6TQv3bt2gVVQ94NLHlY2COPPCKKnBSAU61zoVBAvV7H2NjYkov5Q0ND6OzsPG/U0mg0ROpF/eHJzZ6YmMD//u//4utf/zp+9rOfwWq1YnBwUAQtjUYj4vE4EokEotEojh07JhPpOEmTjBCHwwGn0ylS9YwggDl6n8o8UQ2FmjPlBa+yNdRj4Wfp3bBJU1UIYJ7XbrfD4/HAarXC6/VKDYmgAfJ6vdLbQrltapAZDAahI+rQ8V7B4/Ggv78f3d3dCAQCwtpk6oYNk8ViEblcTgQVWdewWCwYHR1FX1+fKHYA5+5Fk8mE6667TtMU6fV68cgjj0hhPJ/Po1AoIBAISFqpVquJdL3H45GaCf+effZZPPPMMygUCmhpaRFjwQFiXLsoq6JGPOr6QSOkkpW43+o+k+CjsuLo5LK9wGKxoL29HRs2bIDZbEaxWJRIjsrIXBt4rOzLuVSGBVhG5GKz2WTG/Te+8Q0pck1PT4soGgAZGHYhNBoNvPzyy9i1a9d533fq1Cn09/cDgCaSYF8LALS0tODzn/+80A/JzgDmWFT5fB5+v180z8bHxxEOh8VArV27FkeOHJEieCgUEktPD+L111+XuTPNBXyVksgLQjUyNCqLnQdC5fEz70wKNY+FXf0AZH5GKBTCqVOnpM5CUIVWlZPRoeO9Au9Navjx2qex4IKryrzUajVYrVYkk0npz+I9po4fBoAbbrgBf/jDHyR7sWPHDqnTkn3G2U92u132gd/HHjBmMh5//HEMDQ1JZNJozDVA+v1+mahZq9VEt6zRaMjxMePAz6kpcbUuqmZJ6DRT/ZnPqTVmVUMsGo3CYDCIYaEclNVqRSaTwYoVKxAKheT8sPH7UmFZlZz7778fHo9HwkeHw4Hp6WlJwzR76ucDi1nni1oqlQri8bh09KsnxufzwWAw4JZbbsE999wjdZVGo4GpqSmpjZBfzxQRLfr09LTw1YG5mtLk5KRIwZw5c0ZeYw2H39+c7uK2HQ7HvEFAC1GbLwQqs6oDmLLZrPTksLBPtdPZ2Vl4vV64XC4hJnAmuQ4dlxt4j6kD+oBzTpsq16KypYLBoEjrEzRUyWQSR44cwfbt2/H888+jr68P27Ztk34Zo/GcqjgdQbPZjFQqhXq9LlkC1mjeeustxOPxeVRm9uKQrVoul4V5ynQVVdnZgd+c2VCn8TJ6YSYll8tJtEbjqrLnKGhbrVbhdrtFBJO1JLfbjd7eXhiNRlkb2Nvzbsm8LIZlGZe2tjZ89rOfBTDnbVD+ubW1FUajUYptS8Err7yC++6777zvOXToEDZu3Kj5cVibGBgYwPbt2+VEMoSmZTcajfD7/fKj8IJk2MnUU3t7u4xh3rBhA2w2G15//XWRaKjX6zh06NB5ZVLoeZBXzlSU2ui0VPAzTIXRoAUCAXg8HoTDYbS3t8NoNGJkZERYb/l8HpVKRVSpVZxP30yHjksNiiYymiAzrDnVBZzTEnS73ZpZTOp7k8kkhoeHsWPHDtTrdQwPD+PBBx+UfhJ1caUDGo/H0Wg0ZBZVKBTCiRMnJB1+1VVXYWRkBAaDAUNDQ4jFYpIKm5yclNYAysGMjY1JP1pzL08ulxPFZTVtBpxrI1DbINjgzW2pxpdsNj7P0gQbxdkXpPbFtba2XnLDAizTuKRSKfT19QGYY285nU7MzMyIdx0MBhcVpFQxPDyMcDh83vQZi+bNujfkm99yyy1CB240GjLjnshms1L8plfgdrs12yuXy5iYmJATn06nhbFC74QqzYvR9WgMWORjvrNarYqSwHLBi1BNcRmNRng8HnR3d8NkMmkMCzBnQBKJxDy9smAwqM9r0XFZwev1or29HZVKRRqymyVQVOOh3kPMmtBpq1ariEaj2Lhxo0QJDz/8sEQm6n3LyCEajaJYLErEoIrBmkwm/Pa3v8XRo0fx4IMPIhQKwWQyYc+ePXjxxRcRi8XQaDQkbc1jSCQS6O7ulgZM7ovf70cmk4HP55tnRHw+n4wMYR8QR4fw2JlSU8+DmhUhVZvpORoXi8WCUqmEAwcOiPO/YcOGS/L7ym+1nDenUinR62EqqFwua6Sjl7KYvvLKK9i5c+e8YreKgwcPSo2Dnjy70pk24pxsGgeywtQcZ2dnJxwOh1AFCavVinQ6Pc+il0olTE9Pi9Dlm2++iRtuuGHRmona4UqWCMNZTopbKtTwWN1XHltXV5ec8+YivclkQmtrK4rFIux2u0Q+zWMNdOh4r2Gz2YRO7PP5MDk5KXWC5jRZoVAQrTwAspAyFT80NITBwUGRSlGVOhwOhxggprQoyUKvn0XwXC6HeDyOH/7wh1izZg2+8IUvaOpA999/P3bt2oXJyUmUSiXE43E5Fo7eIPOT92+lUoHP5xNjo/brAHOK5CRCAXPrHFlwnBuj6qKpSvFGoxGBQACBQABWq1UMFHCufOD1erFy5colZ5MuNpZlXEhvAyC5x1QqhUgkIp2iF1pMJycn0dnZKU1CCyGRSMDn88lJau5YV9ldFJlTqYRsyvL7/RgYGMD09LTGiJAOSUKAilqthnQ6jWq1in379on0fXNzJdEc6jY3dqoX0/nODWs2nM3NoiONBAeENRoNTE9PY2pqSgqMbrdbWCfs69HlXXRczujs7BRZGCpOJBIJcezYP6cOCQS00lBU5OD94nQ6kc1mpQelecZSKpUSIgAjBNZdDhw4gF//+tf4xCc+gY6ODklJlUol2Gw2JBIJSTPR2aUMlTrIDNBO2+RaoCqJLNYHR02xcrkMv9+P9vb2ea0NKrW5WCzizJkz8Pl8aG9vF8IE0+Imk+k97XFbdmsmf+hAIIBgMIhyuYzW1laNbML5sG/fPtx9992Lvt5oNHDixAl86EMfkryjajiYrmJTpN1uR7FYlJCQaTOfzycGsK+vTy5Yi8UCn88nHo4Ksj8MBgMmJiZgMpkQiURQLBYXNQwGg0FqPPQoVDSrCSy2DR6n1WpFS0uLaKENDg5KaF8sFjEyMiIXc6MxJ/OSzWbR0dEhBuZSMkJ06Hg7MBgMaG1tRSgUkvvz0KFDSKfTcDgc4r2rKd3mXhFe941GQ5qHVSewmeKv9s+w3pNKpfCDH/wAPT09+NKXviRGg/cSi+LUHGN9lccwOzuLqakpeDwezcyaRkMrbMn3n8/BnJychNPpRHt7u9Ri+G+zA2symRAMBtHW1oZ8Po+TJ09iw4YNUqJwOBxoa2t7T0k9yzIu6omx2+3o7e0V+RGPxyNh3GKL6PDwMDo6OjTKpM04ePAg+vv7JVoAzs1eYA8HGSJ2ux19fX3I5XJi2clCaWlpkVCSkYoKagKp+0rtLrvdjoMHD+JjH/uYeC/nOydqtKI+x31vLvI1g0bTbrdLV7HT6ZQ0YK1Ww8TEBFKpFBKJBIC5G0sdG10ul9Hd3S0Xpg4dVwKYlQgEAmhtbcXk5KSMRGd/C1NKamTA9YHFfRJ6gHNevmpsmGEoFouwWCwyXCwWi2Hnzp3SE8fvYUGcBkKNTFTF5nq9jtHRUaxdu1aj2EHjyOzChQxLIpGQNCGglbZq/iyjL/bihMNhrFy5Upq8m2tN7xXe0R643W5s2LABnZ2dwhyjl98c8jUaDRw4cGDRbnzKq9TrdbS1tYmhsFqtkld0u92IRCLCjHK5XGJEpqen5cekJwPMLcLq/GmCoThzp9wHg8GAvXv34pOf/CRMJpN07jdTEnlMKmedzwFzIWmznMxiUC9+boueFTBHnigUCppBRCQz8Oai9D5rMuTz69BxJYEpXkpK1Wo1GTO8UJe7mvZSp9WSVUZjwe0YjUa8+eab2LdvH2655Rbcd999YsCYeaDhomPX3Ium3vdcHyhXo45D57wYOrLNWoQERyf39fVp6sWqUWEEozrmNMxcB10uF370ox8Jo/e9xpI79BcD5zNzznXzj0EcO3YMq1atWtSrrlQqOHToEK655hoAELaH1WqVOSXAHKmANGimyDg4x+/3S8c7u+2npqZEiroZgUAAXV1dcDqdQhLYvXs3brvtNpmZ0mw81OiEjxlhqBcdQ1d1BEAzVDoilU55wVDqmxee+n4aGQrY0RCxkapQKGB0dHTB1J8OHZcrgsGgRqOL1zZFH7l20KunESI9lwaBzlczvTmVSuGll15CKpXCww8/LGO/SSEulUrSR0MSAO9nfkezCvsbb7yBwcFBTV8OP9d8f6vj01WMjo5q5K3U1DqNDf8lWGOimggwRyjy+Xz48Y9/vCgB6VLiHcdOHPkJnEtfNRuYWq2GY8eO4eMf//ii2/njH/+IrVu3arrwbTabLNpUS63VapqohM2C5Mq3tbXJD0zk83lRFk6lUqInRoPU1tYGs9mMf/mXf8H999+PRCIhs2pYNAQgF5B6bDQiJByox65GIypU3SI19cUJduwUpmIzt0eJbdIPOV6a1MNisYh4PC6/RzKZREdHx7J/Ux063gu0trZKnYBZEHr/NDBqBqE5omAaSY1ggLmU08mTJ+H3+7F161a5X9VUGdPt/B5GITQ2ajamUqng4MGD2Lt3L/7sz/5Mk8JXwb4a4FzKvfl9qlQ/95lpL+6D2mxpt9vFiee6pBoY1pNfeOEFfPjDH74ov8vbxbKMy8DAgOYxGxbr9To6OzvFg24+gSMjI9i2bduiNGWOJfZ4PPOai9RGIoPBgHQ6jWw2K81JLpdL8pSpVEqMSzMSiQRmZ2eFasx9zGQymJqawk9+8hN86Utfgs1mw/j4uNRx/H4/stksisXigpEAt6N20RL0ZFTPo5l1phowXkgU2FM9Hh5/NpvF1NSUUDKZOrTb7ZpCp9/vP2+tSIeOyw1msxl9fX1SjD579qwQVlhjVO8hdSgYF38an2q1KoXuQCCAa6+9VuN0srZKkk8wGJR7kQaGJCXSoxOJBF5++WWcOXMGiUQC69at06Sp1GiF/zK912wYiWg0ipUrV8pjVX8M0NaXmAbndzocDsl4EHa7He3t7di3b9+8MeqXGssyLs1eMGcz0EIzDcSwEpg74Wp/TDNmZmaQSqWwadMmAOfSTOwVIa24UCggmUyKZ0LNMHbWq4WvhYxLOp2G3W5HKpXSLO6JRAK/+93v8LnPfQ6tra0YGhpCPp/X1CuYemOo3Ow9NY8YUC9iRim8EfgZgq/TG1HTbuwEDgaDiEajGBsbE4XkTCYjNwIvQOZfaVT0wr6OKw1utxsrV65EPp+HwWDA4cOH4ff7pR9EjeSpNcbFlk2FNEZ2ux2bN2/WRB1kgTEjQkYY7yc2PlLokrWaH/zgBxgbG9P0lzU722pKi0wydR1ort1OTU0tKCTJDAm3w/9Xq1URqOW6yFQgv4Plg82bN+O55567oHbju4l3VHOhXLXBYJCGRpWqB8wJTzb/CMTs7CyOHj2KDRs2LNhQSd42F2AOAKMxK5VKSKVSGB0dRaPRkNxpM5ifpbdPvPHGG9i/fz8eeOAB0Tlqlq5WZa/VtJj6/+aIRaUOqpLaaqGO/TicKaFKOtCYTU5OIhqNCs1xampKM6+F2y2Xy1KjUUVEGdHp0HElgWoUg4ODCIfD4jCqBBm+T40cMpkMJiYm0Gg00NnZiZaWFvksU+1q3bJQKEhUQLkmn88ntReHwyE6fgMDA5qCvMvlwvr16xfUDqNhGR0dRSAQ0CigE5xb06xA0gx1raHxZFmATaFs5FQHfzkcDqxcuRJnzpx5h7/G28c7Mi65XA79/f0olUqiCkqvgEYgn89LcVwF2WPN4aq6aNvtdulCVcNEeuRMO7HIz5C6GewGJiYmJrBnzx50d3dj+/btmvHKpAQDc7Wa6elpZLNZOb7mwtpSwGNjVGc2m+HxeOD1ejV5WOZaqVNWKBSQSCRw4sQJzMzMiH4QBwmREcPeHpUu6fP5dOOi44qG2WzG1q1bRQ38fPcdI4qOjg5NDZiLv5oNIZWYzh8ATW1H7YdzOBwol8tIJBLYsWOHbLutrU166ShQyexBNptFNBpFb2+vRB7NEcvPf/7zJY8mUcHtUSBz/fr1aG9vRyAQEMNiMBgQCoWwadMmHDp0aNnfcbHwjgr6Ho8HXV1deOONN+B0OoUizPTOiRMnsHr16gU/e+jQIaxfv14aJJu9f9YbyF93uVyYmZkRaiH7XABIaEhVUHbp0gNyOByYnZ3FyZMncezYMXg8Htx8880Azo0DNpvNsNvtCIfDGBsbk/rMQiyx89GKFwK9Jm6D+8WLTi36swufqTbWhKi4rDJZuG+kaVPjrLe3F+3t7cvaRx06LjcUCgVEo1FRX/f7/ZoeE4KtB62trZrn1DQzcO4+JMi2pLIxdb6MRqNMbfzlL3+JyclJPPDAA3C5XBgYGMBTTz2Fzs5OyThw/apWqzh58iRsNhtWrFihaY0A5qKlPXv2YP/+/RgcHFy0Br0Y6IhyXHkmk5GplNlsFrVaTXr8KPu0efNmvPrqqyKldSnxjoyLxWIRtpjFYpGDZK4SwIKRRDweR3t7uyiFEqqnQTlpNiHl83mcPn1aLiyyw4xGI8LhsNCh+/v7EQwGZcznyZMnpU/E7/fj9ttv19RdXC4XMpkMvF4vJiYm4Pf7ZWoee0jy+bwUE9V86FLBC4xGoVqtIpfLSZhLqIV7tcbDc202mzVKCIyEVEXUQCCgRyw6rnhQaXhmZgbFYhHBYBDxeHzBazuZTIosVTPo6assVDqjTJVTPorDv2w2G37729/iiSeewMc//nHs3LlTHNu1a9fis5/9rKxLjHZMJhPOnDmDgYEBTaTE9eK1117DK6+8gunpaQBvb/S42tJQLpeRTqcRi8UQCARk5lUzIpEIhoaGlv1dFwPL1hZTYbVaJVpRw8pSqYSpqSk50c2efltbm/RwqOkwpoQoyhYKhXD69GkUi0V0d3dLTYHv56jg7u5u+Hw+JBIJnDp1Cu3t7dIfMzAwgHXr1gGATL3z+/1C4c3n85L3zGazyOVyCIfD6OjowJkzZ1AsFuc1P12o27YZ9KJ4jKoSKs8rt0l2CVkg9KD4Hs6QoLfk8/kQDoclJLZare8pQ0SHjuVgfHwcFotlXmFbZXRWq1WZphiLxYTZRTAdpVL8aUjUXjM+ZkuDWignEWBkZARPPPEEPB4Pvv3tb6OtrQ3j4+PyXSaTCf39/bJ2mc1mmV9/1VVXSS2FDY/lchm/+c1vsHfvXs3xvd0xGMza8PjK5TJOnz4tgwEXwk033fS2vuudYlnGpbn4RANBOXu1qZHh20LpLi6krB2obCemp+LxOEZGRrBq1SoMDAwgn8/D6XQiGo0KG4oDtQDIvGhg7ofr7Oyct//U46GXc/bsWczOzoqwpcFgEIVTLuak+alRhsr6WKqhURuv2Dms5mnJuiOX3e/3iyS32WzG1NSUDApjkY8UZP4GFosFg4ODuraYjisCnDUyMzODRCKhmZpIJ5JFdd6LgUAAU1NTQiQieC+p6WveW7ynVSOTy+U0aeXh4WHs3r0b8XgcDz74INauXQuDwYDJyUmYTCa0tLRIyp/bYy+cKjej6ovV63UkEgmsXLkS1WoVb731lhiiCxXyF4LKRmOjOCOz5lEblwPeUVqMTUctLS3IZDJzGzSbZbFWOenN/G+eJDYKcrFmmhBRKMcAACAKSURBVCoYDKKlpQWlUgm1Wk3mv4RCIRSLRaRSKfFoQqGQJqpaTGaBn2NPCuc6ABCF01QqJX0kamMTDSHBSE2VfLgQ1Nwrzw//WD+q1+vw+Xzo6uoSD4VyMGSFUaaCXozX64XT6cTVV1+tS+zruGKgjuk+evSoZm4SHTiz2Yy2tjak02khsLS3tyMWi6G1tVWow3TQuNZwUBgbDMnWYsEemLsPyRh1u93Yvn07Vq1aJcO1OE6Y/SRcp2i4eA9aLBZhemYyGYyNjYmkTEtLCwYHBzE4OIitW7fiN7/5DU6cOKGpDy0FqiGlYgn3RZ2AeznhbQtXAnOGpFKpoKurS7rey+WyzKhXu/VVz13teOfoUf5glUoFLS0t0jxpt9sxNjYm+jyMLqgGyvqMioU0tbLZLCYnJzWh9uTkpMhMcCY2ADkWlQJJhhepymrOdjHJm4XACEmlKwMQmrXNZsPAwADa29tx/Phx1Ot1xGIxFAoF8YzU2lQ2m0V3d/d5B5rp0HG5Y82aNTh48KDIP/l8PtHL6+joEMYmDYfdbsfo6CgikYjcE5z5ZLVaEQqFJO1NR65arUqa+ZlnnsGJEyewZcsWfPvb3xZ2JlNd2WxW0mVqDx/T0nQI8/k8TCYTTpw4IRMn169fD6fTKekxrhGhUAgPPfQQXnnllUVrRM1Q1UroNLO+Skat3W5fNoP1UmBZqxGNiTpnpb+/H1arFa2trZienhYGFAd3LVT8Zu2BaR12oXP0KY2F0+kUGYjZ2Vm0t7cjn88jGo3K4tysdmw0GufVHBqNhsw/Ud/HLlxGIQR1icgaUcNpMrasVis8Hg+SyeTbqsGo/zL15XA4EA6H0dbWhmg0inK5jFwuJ5IvTDfyQuI+zczMIBwOI5/P61MndVyRMBgMCIfDmJycREdHh2QtCoUCAGBwcBClUklYUXRKR0ZG0NXVJTVaNQsQj8dFaRyAdOy7XC7ccMMN+OIXvyjrD6MZrkHMpNChZdagUqnIPjHr8vLLL+Pqq6+Wlgk6zezHU+VhGo0Gtm/ffsH1Qm1L4B8pzyw5MMXP/pzLDcsyLmRtqTl99o+0tLTA6/VieHgYvb29Ih6nauMA57rGOQ4YgPSzkB1Fg8HufgASfqp9NOFwWMgD4XBYFufmBZYKwioY8WQyGTEYACQUZt8IvQNVn4zfzwtd7b5fDmhQ6Z0wFTgzMyOTMDOZjIj3MQ2nficvYpU2rUPHlYiuri7s2bNHmgGpgM77MxAISN2CrQiVSgUejwebNm2C2WyG1+tFS0sL3nzzTdjtdmQyGRw8eBDpdBqRSATXXXedSDrxHqOxYuNkqVQSR5rkIho6ZhcMBgPGxsZw/Phx2aaa+mdduNnBVmtC55t/xfcxo0GHlrI3oVAIPp9P9u9yTIdf1DyKxWJBW1sbrr32Whw+fFjkD5ibVLtkqYvFPGg2m5UIhlDTTWRlGI1GoTeTQcKQc7E85kKLLq29wWCQIhswZ1woo8KFnZLfFosFXV1dQgfmgr6UIWmL7RepkcwdF4tFOJ1O5HI5JJNJOW/qRco0Iw0dmTJvl4GiQ8flgu3bt2PPnj249dZbAczVYNWF0263Y3h4GMViUZosh4aGZFIrnS6r1YqjR4+iXC7jrrvuwszMjCz4jUYDra2tsNlsImBrMMzpFlLXjw6kqsxOHT8aljfeeAO33367GCIaOw78MxjmxoiorRncNu9hVUlZlaihQjrTfE6nUx63tLQgEokgHA7D5/NdttmKZRsXsrMWg9vtRjgcxsDAAI4fP66Zy8AfS/0DIFGAzWZDMpmUqZK03sx5lstlOenAuSjofLMSgDnrz74Yhtaq4QuHwygWi6KVxv1irlZVDZientYIbDJEfjtgiOtyudDb2wuTySQSL/l8Ho1GQwqQ/D56POx7oSZZV1eXXnPRccXDbDZjcHAQR48exdq1a+e97vP5sGHDBrknLBYLYrGYpIyAuZrpW2+9hQ0bNsg9wVoLADFCnKbb09ODRqOBU6dOaZxbpsU5Tp3isuPj49i7dy8+9rGPSYrfYrGgu7sbVqtVeuQMBgOCwSCGh4fFUWQ6j4of/B62VhB0oEmf7u7ulsmca9euvSKapN/2mOPzvR6JRGCz2RAMBnHgwAGh31IThwbC5XJJgXz16tUIBoNIp9N45ZVXkM/nsWnTJrmIzGazRBFer1cYHAQF6AwGw4IF/ba2Npw6dUqamAh2sxaLRSSTSdjtdqTTaaTTacnDqhpi1PLyer2aiGe5UJl0ZKIlEgkJ1xktqU2Tqggmw2Sv14uurq7L1nvRoWO5iEQi2Lt3L7LZ7KLRuCrnNDg4iFOnTmHVqlUAAK/Xi7vuugsTExPyHofDIRp9/CwdS64X69atQzgcxsmTJ0X3i8oX0WgU1WoVR48excsvv4ydO3dKepqsNJfLJeuV2jzOHhQ6hTSCTMex9ktQjd1kMonDazQa0draKhHLlQBDY7laJstAo9HA0NAQzp49K8N6uFCSK84u/VAopNH4SqVS+Md//Efcd9996OzsFDG4XC4Hu92O/v5+8TKoYszHpCs2qwOcPn0ayWRSoimXyyWeTSQSwfT0NMbGxpDNZjE8PCz1F3oVqqYZjd1Sacgq6HHxgmGtiBEcj5Pdyc0/EWmWXV1dWLlypTzu7u5e9r7o0HE5gtqDGzZsWFLfFnvAVGSzWUxPTws5x+fzSeqZzu1CjYe1Wg2Tk5PiPHLtefPNN/HTn/4U9957r4wbIXPN6/Wira0NpVIJ6XRaY/w4sgOATNW12WyYmJhAsViUlgKm+hhRGY1GScVt2LBByEVXCt7VPTUYDOjr64PX68XY2JjkF8kkYyqLXjjhcrmwYsUKfOc738F//dd/YXx8HDt37pQZDVzwuQ0WuYjZ2VmMjY2hr69PQ3uuVqsLimgC52bQkzaoan8xUlBHD1Oe5e2cE3U0Abn7jAhdLpfkjGnEGK0wciL9sKWlRdPkqUPH+wUGgwHXXnst3njjDaxZs+aCkflCr3s8HrmPm7X4zgemodRR4rt378axY8fwF3/xFxgfH5caCQf7ORwOcXDNZrNG2cNms2F2dlaYoKqQLafMVqtVxONxmM1mhEIhWc/UNfRKw7sauTSjUqngrbfekgU/Ho/DYDDMUy3u7OyE0WjE2NgYAGDfvn147rnn8JWvfAU+nw+pVEroeQwbGUaqCIVC0rXKnCoXYZVqCEAYIfl8HsPDw8hms0in02JcWKtRh/csp7+FYGGQxoEFO5U+zW2OjY3JPqjT6Pj+jRs3ath6aoezDh3vF+zcuRNdXV342Mc+hrvuuuuSK1A8+eSTyOfzePjhh1Gv1zE1NYVYLCZ1WpvNJusPlZRjsZj05lAlQHUigbl71u/3Ix6PY3Z2Fk6nE7FYDIlEQpojI5EIurq6rkgm6CU1LplMRuos7JhVRw4bjUah49ZqNZw5c0aMQTqdxj/90z9hcHAQt912m6TKotGo5DLZsUv4fD5N4SsWiyEej4uaKADpV2HetVqtIhaLyThkCttxPwFo5q8sJ2JgtEK6ZEdHh0hEcBYN6c/1eh2FQgGFQkEMDHBuLMC6desQiURku729vVdUyKxDx1Lx2GOP4Qtf+AIAoKenB7fddhvuuOMOfOITn1iwvnox8d3vfhcdHR247777NM+zORKATOPlqGQAcv+SsGM0GqVvDZiLqhaLoJgVUUlNVyIumXHJZDLweDzzTlaj0cALL7yAqakp7Nq1SxN9JBIJTQG+0Wjgsccew6uvvoqvf/3riEQiGB8fF++gq6tLw2YLhUJwuVxSnG80GhgeHtaks0gWYIQTj8dFxoFssVQqhWQyOY+1pf5dCNRMc7lc8Hg86O/vl0l6nJLJAWg0XolEQujQ6v56PB6sWLECkUgEHo8HwWBQ1xPT8b5FNpvFypUrkUgk5Lk777wTTz311LtGv69Wq9i9ezecTie2b9/+rnzH+x2XxLjk83k4HI7zWuFf//rXcLlc8xQ80+k0UqkUKpUKzGYzMpkM8vk8/v7v/x7hcBgf//jHZfF1u92IRCJSLA+FQojH4yiXywAgYazL5ZLah8PhkHkptVpNWBv1el0Kf7lcDlNTU9Khy94Wte/kQqDCMft0QqGQDAtjWo+EBz6XTqcxNjYm8jOqeF84HMa11157QWq4Dh3vB3z+85/H97//fQDADTfcgP/5n/9Bb2/vu/JdJ06cQDQaxQ033KCPCn8HuCQxF8PC8+Huu+/GxMQEDh8+rHne7/ejr68Pq1atwsqVK+H1ehEKhfC1r30NfX19+I//+A/MzMyInAuZGl1dXZiYmEA0GkUikUAikRBF5WKxCI/HI/vFbnwaIWCOpuhyuRAMBjXDuJprLUu1zZVKBcViUeo60WhUBD59Pp80TrW2tqKtrU1oiIyyWGfxer0aUT0dOj4I2LlzJwDgxhtvxE9+8hOEw2EkEom33cC8EIrFIp5//nk4nU7cfPPNumF5h7iskvQPPvggvve978FisWDNmjULvsfv92N0dFQ0erZt24bdu3cjk8ngoYcegsfjEdmIeDwuxoANlJxQyY57anVRyoX1DgCiqMrPAOcGegFaw7IUfTHqg9VqNUnTkZDw9NNPY9euXbBYLMjn8xgdHcX09LQ0WzEHS4YZaYo6dHwQcN999+Gee+7Bf/7nf0oK2+FwIJ1Oi9LGO0EsFsPZs2exY8eOK7J4fjnisjIuAPDlL38Z3/zmN7Fjxw7cfffd814PBoPIZDIyTdJoNAqD5Gc/+xlCoRBWrVqFmZkZNBoNlEolKaI1Gg1pluzs7JSO+3K5DJfLhWg0ikwmI4yumZkZkYRgMZ41loVGrV4I/Cy1yzKZDAqFAmZnZzE+Pi7EgVOnTiGVSmm6eLmvbW1tcLlcCAQC73oxU4eOywVWqxU///nP5y38Pp9Po7T+dtHW1oa2trZ3tA0dWlxStthS0Wg08K//+q8wGo348z//83mv12o16aIllZeYnJzE3r17sX37dpHVZy8Mo5h6vY7e3l7Y7XY0Gg2Rq49Go8ISU1Ng/AxZHFRNfae9JUzJ2e12HDhwQFhwo6OjIqjH1BdvnlAohPXr16Orq2vZMyF06NCh41LhsuS5GQwGPProo4hEIvjmN785L0owmUxoa2uD3W7XGBaDwYDrr78e3/jGN1Aul/Hyyy9rqL0syHu9Xs3kR6fTKekpagmxkdHhcMDpdEo0sxRbHI/Hl2R4GAGxM/f48ePSkU+jQ9485SWCwSAikYje06JDh47LGpelcSF27tyJBx98EI8++ug8gchAIIDu7m643W5ZhHt7e4U9tX37dgwODuLQoUPzFmw2QXGWNiUYgDnOOllZNptNjJfH44HP54Pb7RZV08XgcDgQj8cveHzcRr1eh9frRTQaFckJ7i8ZbW63G8FgEH19ffNGvOrQoUPH5YbLrubSjI0bN+Lv/u7v8Nhjj+H666/Hli1b5LVmOW4V9XpdZLgPHTqEarWKgYEB2Gw2eL1euFwunDp1CpVKBWvWrIHb7dZMn2Qaios8mWRUIT4fPB4PYrHYko6PRqK1tRVDQ0NoNBrwer3ChOF+cI63HrHo0KHjSsBlHbkQLS0tePTRR1EqlfDd7353STL3ZrMZPp8P4XAYN9xwA7q7u3H06FGhLHd3d2Pbtm1YvXo1pqenMTw8LDL3FJZjrwtTYgDmDT97J1AZaF6vVxhhHONM+X+73Y5IJIIVK1YIU0aHDh06Lmdc9pGLiptuugnXXXcdvv/972P9+vW48cYbF30v6xShUAjZbBZ9fX3w+/348Y9/DJfLhV27diESiSAQCKC9vR3RaBRHjhyB1+sVKQdup1qtIpvNaoYRvROQtkxBSofDITPDHQ6HzIGgHDgHoVFBWocOHToud1yWbLGlYN++fXjzzTexc+fORVNF5XIZ4+PjwhZLJpNIJpP49Kc/jUKhgPb2dnR1daG7uxtr1qzBbbfdJjO5i8UiZmdn4fP5kEgkhNk1Pj6OXC53QQMzMTEBn8+nSdst1EjKNF0wGMT/+3//D9/5znfQ0tKCer2Ojo4OjYqqDh06dFwpuCLSYgthy5Yt+OxnP4vnn38eTzzxxIKTKG02G/r6+tDZ2Ym2tjZs2rQJ/f39uPXWW9FoNDA5OYnXXnsNzzzzDA4cOIDp6WkUCgXkcjmR0PZ6vbDZbFLEp2zMhdDW1oZoNCqP1fnaHJ3qcDjgcDhkhIDBYIDX64Xdbkd7e7tuWHTo0HHF4opKizXDZDLhU5/6FNLpNL7//e9jw4YNmoI/cE7oEZgr8k9PT+OBBx7Ac889J8ysq6++GnfffTfS6TRcLhdOnz6Nvr4+GAwG0RdjgMc6CLv8F6u/UKZfhSpyaTKZEAwGYbfbZVKn0+lEV1cXPB4P3G73RT9fOnTo0HGpcMVGLir8fj+++MUvAgCefvrpRd9HPa5QKITNmzcDALq7u/GZz3xGaiCq/Au78in5wsFeNBLLkWBRFZTVgWMAYLfbpb+mo6NDNyw6dOi44vG+MC7Eli1bsGrVKoTDYXz2s5/FP/zDP+D48eMSLbAg32g0cOeddyIUCuH666+XukizfhjTYU6nE7OzsyLdbzQapXOfRme56atKpYJsNotMJoNyuSypsqmpKeRyuYvCRtOhQ4eO9wrvK+MCAFdddRVuu+02PP744/j617+OTZs24eqrr8bOnTsRj8cRDodhsViwcuVK/OVf/qVmxgx7WlasWIFYLIZGo4FsNiuvU2eM0yj5GdKbVZzP4LAzv1wuI51Oi/rxa6+9hmPHjmFsbGze3BkdOnTouJLwvjMuAPDXf/3X0qlfKpXw5ptvorW1FR0dHVi1ahVCoRDq9Tq6uroAAE6nEx0dHfB6vejs7MSKFSvQ2tqKdDotCsWxWAzValWEJ1VlZKbTVGPCLv/zyeJTJTmfz6NSqaBWq6FSqSCfz6NcLi+5EVOHDh06Lje8L43Lxo0bcfvtt8vj2267Df/2b/8GYC7lFYlEsHbtWnR2dqKlpQWBQAAejwehUAgOhwNerxd9fX0YHR3FsWPHkEqlxKBQsJJ/i02kpKRLKpW64P6WSiWMjIxILYZNovl8Xo9edOjQcUXifWlcAODhhx+G2WzGwMAAHn/8cU3hPZfLIZPJwGq1YsOGDRgeHhaxysnJSYyOjiKTyaC9vR1utxvDw8OYnJxELBZDJpNZ8j4YDIYLvp8GKpPJwGAwyLwXAJrivw4dOnRcSbiiqcjnw3333Ydt27bhmmuuQXt7uzzfaDSQTqdl0e7q6sJbb72F1tZWVCoV+P1+MTIzMzPI5/NoaWmRqGRmZkZqNxfCUgaIcZ+KxSLsdjuSySR6enoAzLHV9IFgOnTouBLxvo1cDAYDnn/++XkDgAqFgrDGOJ8lnU7LjBar1YpisQgAUgdRDYTX64XH48HExMSS9uNCxoVTJcvlMhwOB0qlktSLdPVjHTp0XKl43xoXABplY0JtesxmsxKdVKtV2O12mM1mVCoVMT4cK6zC4XAgFAphdHT0vN+/VMNgsViQzWbR2toKj8cDs9mM9vZ2+P3+ZRytDh06dFw+eF8bFwDo7e3VGAGr1YqRkRFMTU0JpZi9L4VCATMzMzJemMZhIbkXq9V6wWI702KqkWnWF2MfTS6XQ09PDwYGBtDb2wufz/dODluHDh063lO8743Ltm3bsHfvXnk8OjoqQpa1Wg35fB6zs7PSzMjXKPtyPh2x06dPa7bdDBoXRkpGo1Hzp0rJJBIJRCIRWK3WRWfU6NChQ8eVgve9cenv78fMzAwAiCglZ7XMzMygWCzC7/eLzlg+n4fVahWpF9Y/FmqUtFgs+P3vf4/XX399we+enZ2Vz9CgAHORkNVqlccOhwNTU1Po7e3VsMV06NCh40rF+964GAwGWaxLpRJKpRIymQxmZ2elJtPa2ipKyBaLBbVaDX6/H36/H3a7HTabDTabbV4Uw1rNr371K+zbt2/ed+dyOdjt9nlpMRIKTCYTLBYLXC4XotEoNm3aBKvVinQ6/e6dEB06dOi4BHjfG5dmFAoFWCwWTf+I3+9HIpGA2WyGwWCQGStutxtOpxNOp1NowTRIACTamZ2dxbPPPjsvRZbL5WTAFynFJpNJU3ehpL/RaJR0GNlqOnTo0HGl4gNjXFSpe4/HA4vFInUX4BwluFKpyOJODTCz2Yx6vS60ZBqHXC4n269Wq9i7dy+Gh4fluVwuJ9plJpMJTqcTLpcLVqsVdrsdgUAAPp8Pk5OTuP766+VzS5kXo0OHDh2XM963TZQqOMbYZrOhpaUF09PTCAaDmJ6eRqPREPXjQqGAWq0Go9GIZDIJr9crul9kfTG6qdfryOfzsFgs6O/vR39/PzZu3Air1SrfWyqVJC1Wq9XEoKiilk6nE3v37tVM0/R6vZf8HOnQoUPHxcQHwrgMDg7i5MmT2Lp1KywWC9ra2lAul1Gv1yUqsVgs0utiMpkkenG5XPKYzC/WSzZt2gS3241NmzYt+t1WqxVutxu1Wk0aNplu83q9sFgsOH78OO69914AEJ0zHTp06LiS8YFIi61cuRITExOw2Wzw+/1SA7HZbKhWq0gmk+jo6EAikZCIolarYXZ2FrOzs6hUKlI7YSqtVqvh5ptvht1uX/R7HQ4HGo0GSqUSqtWq1G+CwSCCwSCsVit6enrw5ptv4tZbb0VfX9+SZGV06NCh43LHB8K4OBwOkYFpa2tDOBxGNpsVI2EymRAIBHD27FmUSiXk83lJfRUKBdTrdamDUA25ecQxazb8owgl38/P0kAFAgG0t7fj2LFjGBkZwcDAgCalpkOHDh1XMj4QxmUh+P1+SU+xzsJah8ViEVYX6yNselSZXnysClSyeJ/P5+FyuaTeUq1WJdVmMBiQz+eRz+fx+c9/HgCkF0eHDh063g/4QBoXzkupVCoA5uoqFosFFosFdrsdXq8Xfr8fHo9HtMVoYFStMVWzTO3GNxqNGB0dRUdHh+Z1Tp+02Wwol8t49NFHkU6nYbPZ8OKLL17CM6BDhw4d7y4+UMZFpSMD5wyC3W6HxWJBe3s74vE4qtUqDAYDVq9eDZ/PB6fTKb0xampLlXahAaIR+cUvfiFd+EajUYgAfO+///u/4/Dhw6jVaigWi/j5z3/+3pwUHTp06HgX8IExLj09PSJgyfSXx+MR/TCn04nW1lYkk0kZTZxIJABARhGThkyDUi6XNU2VxO7du5HL5XDixAlpvORnTSYTXnjhBfzsZz+TCAoAnn322Xf1+HXo0KHjUuIDY1xWrFiBoaEhAHO9JaFQSJhbrKOw9mIwGJDNZjE9PS21EfbAWK1WWCwWGI1GZDIZBINBTfQyNDSEV199FfV6HUNDQ5pCPyVkHn/8cdEXI5LJ5JJGIuvQoUPHlYAPRJ8LMJcKu/baa+VxS0sLPB4PAoGAjDAGgHA4jHq9Drvdjmw2i0qlgtnZWaEgq4ywVCqFwcFBAHMpt3w+j5/85CfIZrMAIMbCarXCarXCbDaju7sbr7zyCqanp/FXf/VXcDgc2L9/PyYnJ3HgwAHcdtttl/jM6NChQ8fFxwfGuACA2+3WPLZarQiHwwiHw9i7dy+q1SpWrFiBWCwm8jCVSkWoyLVaTR43KyTX63X86Ec/wtTUlDyXSqVw8uRJbNy4UWo6nZ2dKJVKWLt2LTo7O/HYY48BmNMpowKzDh06dFzp+EAZl8VACRhOoqxWqygUCigUCiiVSmJM2NdCuX61k75UKsHhcKCnpweJRAL1eh2lUgmHDx/Grbfeiq6uLpF4MZlMOHbsGNavXy+f12e46NCh4/0E3bhgrlbi8/mQzWalo352dhbVanXefBUW9GOxGFauXCnPOxwOfOpTn8KJEycQCoXg8XgQjUZhMBgwODioiUq8Xi+effZZPPLII5f0OHXo0KHjUkE3Lv+HlpYWlEoljI+Pi96Y2reiPs7lckIEaKYiT01NYePGjQiFQti+fTtMJhMcDod8D6nNs7Oz89J0OnTo0PF+gW5c/g9+vx/1el2aGlXxSva98P+lUgmtra2aZkqDwYBUKoW2tja0trais7NTJku63W7Y7Xb595lnnsHOnTvfs2PVoUOHjncbHxgq8lIQDAbR1dUFv98Pp9MJt9st81doSKamptDe3i6d+pSFMZvNOH36NG688UZ0dHSgs7NTBDJbWloQCoVE5HJ0dBSRSOS9PFQdOnToeFehG5cm2Gw2jfEwmUyw2+2oVCrIZDLo6ekRWjFJAG63G4cPH8Ztt92G3t5edHZ2Sg+L2WzWKCe/+uqr2LJly3t1eDp06NBxSaAblyaYTCYMDAygtbVVGioLhQKCwSB6enrgdrvhcDjgdrvh8/kQCASQz+dx7bXXIhKJoKOjAzabDcBcHaatrU1DWz506BC2bdv2Xh2eDh06dFwS6DUXBclkEsFgECaTCV6vFy0tLRgZGUFHR4dMqqxUKqKYXK1WMTs7i3Q6jQcffFBqNW63GxaLBX6/X9OFv2fPHnzoQx96D49Qhw4dOi4NDA1V2vcDDPa2NI8YrtVqyGazqFarMJvNqFQqOH78OMrlMgwGA06dOoXt27fDYrEAmNMr6+zsnLf9ZDKJZ599Fg899NAlOR4dOnToeC+hRy7/B7PZvODsepPJBL/fr3mu0Whg//79KBaLuOWWWzSvqbRj9f1PPvkkvvzlL1/UfdahQ4eOyxW6cVkm6vU6jhw5Aq/Xq2miBObkZHw+37zP/OIXv8BHPvIRkdzXoUOHjvc7dOOyDCQSCRw5cgQ33XQTGo0GEomEjET2eDxoaWnRTKoEgImJCdTrdfT29r5He61Dhw4dlx56zUWHDh06dFx06FRkHTp06NBx0aEbFx06dOjQcdGhGxcdOnTo0HHRoRsXHTp06NBx0aEbFx06dOjQcdGhGxcdOnTo0HHRoRsXHTp06NBx0aEbFx06dOjQcdGhGxcdOnTo0HHR8f8BfJMqCm6oRI8AAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 500x500 with 1 Axes>"
       ]
@@ -634,7 +705,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -653,31 +724,27 @@
      "data": {
       "text/plain": [
        "chr\n",
-       "chr2     686\n",
-       "chr1     605\n",
-       "chr11    553\n",
-       "chr5     523\n",
-       "chr4     509\n",
-       "chr6     489\n",
-       "chr7     485\n",
-       "chr3     471\n",
-       "chr10    464\n",
-       "chr9     458\n",
-       "chr8     421\n",
-       "chr12    397\n",
-       "chr13    370\n",
-       "chr15    362\n",
-       "chr14    345\n",
-       "chr17    322\n",
-       "chr18    298\n",
-       "chr16    266\n",
-       "chr19    252\n",
-       "chrX     182\n",
-       "chrY       1\n",
+       "chr11    6\n",
+       "chr4     6\n",
+       "chr6     5\n",
+       "chr1     4\n",
+       "chr5     4\n",
+       "chr7     3\n",
+       "chr3     3\n",
+       "chr13    3\n",
+       "chr16    3\n",
+       "chr18    3\n",
+       "chr10    2\n",
+       "chr17    2\n",
+       "chrX     2\n",
+       "chr9     1\n",
+       "chr12    1\n",
+       "chr14    1\n",
+       "chr15    1\n",
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 97,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -694,7 +761,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 26,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -711,16 +778,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(8161, 8459)"
+       "(96, 50)"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -731,7 +798,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_seqs = ad.shape[1] # 10000 # adata.shape[1] # None # 1000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -743,28 +819,27 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/tmp/tmpqlfmo0ty\n",
+      "/tmp/tmpkd0osjqm\n",
       "genome mm10 True\n",
       "annotations/mm10/genome/mm10.fa\n",
       "True annotations/mm10/genome/mm10.fa\n",
       "running bedtools...\n",
-      "['/home/ilibarra/miniconda3/envs/mubind/bin/bedtools', 'getfasta', '-fi', 'annotations/mm10/genome/mm10.fa', '-bed', '/tmp/tmpqlfmo0ty', '-fo', '/tmp/tmpttofrsjk']\n",
-      "/home/ilibarra/miniconda3/envs/mubind/bin/bedtools getfasta -fi annotations/mm10/genome/mm10.fa -bed /tmp/tmpqlfmo0ty -fo /tmp/tmpttofrsjk\n"
+      "['/home/ilibarra/miniconda3/envs/mubind/bin/bedtools', 'getfasta', '-fi', 'annotations/mm10/genome/mm10.fa', '-bed', '/tmp/tmpkd0osjqm', '-fo', '/tmp/tmp1u8kp5bs']\n",
+      "/home/ilibarra/miniconda3/envs/mubind/bin/bedtools getfasta -fi annotations/mm10/genome/mm10.fa -bed /tmp/tmpkd0osjqm -fo /tmp/tmp1u8kp5bs\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "8459"
+       "50"
       ]
      },
-     "execution_count": 100,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "n_seqs = ad.shape[1] # 10000 # adata.shape[1] # None # 1000\n",
     "seqs = mb.bindome.tl.get_sequences_from_bed(ad.var[['chr', 'summit.start', 'summit.end']].head(n_seqs), genome='mm10', uppercase=True,\n",
     "                                            gen_path='annotations/mm10/genome/mm10.fa',\n",
     "                                            bin='/home/ilibarra/miniconda3/envs/mubind/bin/bedtools')\n",
@@ -779,16 +854,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "8459"
+       "50"
       ]
      },
-     "execution_count": 101,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -799,7 +874,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 31,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -818,16 +893,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(8459, 8161)"
+       "(50, 96)"
       ]
      },
-     "execution_count": 103,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -838,7 +913,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 33,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -852,7 +927,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 34,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -867,16 +942,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(8459, 8162)"
+       "(50, 97)"
       ]
      },
-     "execution_count": 106,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -887,16 +962,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(8459, 8162)"
+       "(50, 97)"
       ]
      },
-     "execution_count": 107,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -907,7 +982,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 37,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -918,10 +993,10 @@
     {
      "data": {
       "text/plain": [
-       "(8459, 8162)"
+       "(50, 97)"
       ]
      },
-     "execution_count": 108,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -934,16 +1009,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(8459, 8162)"
+       "(50, 97)"
       ]
      },
-     "execution_count": 109,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -955,7 +1030,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 39,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -969,7 +1044,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -1004,15 +1079,15 @@
        "      <th>8</th>\n",
        "      <th>9</th>\n",
        "      <th>...</th>\n",
-       "      <th>8152</th>\n",
-       "      <th>8153</th>\n",
-       "      <th>8154</th>\n",
-       "      <th>8155</th>\n",
-       "      <th>8156</th>\n",
-       "      <th>8157</th>\n",
-       "      <th>8158</th>\n",
-       "      <th>8159</th>\n",
-       "      <th>8160</th>\n",
+       "      <th>87</th>\n",
+       "      <th>88</th>\n",
+       "      <th>89</th>\n",
+       "      <th>90</th>\n",
+       "      <th>91</th>\n",
+       "      <th>92</th>\n",
+       "      <th>93</th>\n",
+       "      <th>94</th>\n",
+       "      <th>95</th>\n",
        "      <th>var</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1042,7 +1117,7 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>ATGTAAACAGTGGAAATCAGGGACATAAAAGGAATGGTGATTGATAGTGTTTGTACCCAGCAGTTTGTCACTCTAGTTTATCCTCTGTCTGCATAGCAGT</th>\n",
+       "      <th>ACCAACAATGTCTATAAATGTTCAGATTGACAGAATGGGTAAATTTTTCTAAAACATTCCTATGCTAAGGGTCTTGGCTTCAACCAGAGGCTGATTTTGC</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -1058,15 +1133,15 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.006698</td>\n",
+       "      <td>0.061404</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>GATACCACCAGATGCTTTATGTTTTGTTGGTTGTTTAGTAATTGGCTACAAGGAGAAACACACACGCACACGCACATGCACACGCACACACACACACGGG</th>\n",
+       "      <th>AAGGCTGCCCTCATCTACCTTCCCCTCCCACATCTCAGAGACCTCAGCCCAGCTAGGAAGCATTGGTTTCCAAGCAAGCTACTTCCAGTGTTGCCCAGGT</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -1087,10 +1162,10 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.022447</td>\n",
+       "      <td>0.030592</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>AAGCCATTGAGTGGTCAAAGTTCAGTGAGCTTTTCTGGGGAGCTGGAAGGGTAAGAATGTTAGGATAAATGCAAATAATGGAGGCCTGGCTTTTGAAGTT</th>\n",
+       "      <th>AGCTGCCCATCTGTTCCCTGGACTGCTGGGGTGACAGGCATGTGTTACCACTCTCAGCTCTCACTTGGGTGCTGGGGATATAAACTCAGGTCCTTGTCCC</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -1110,11 +1185,35 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.030592</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CTAAAGTGACAGATTAGTATATAGTTTTATAATTTGTTCAAGTCAAAACAATAGGATAGATGCTGTAAATTGTGTTTCAGTATTTGTTAGAAACCTGGTC</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.015358</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.115351</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>GCTGTGGGAGGAATTGGCACAGTTTCACATGACTGGTTCATTTAAACATCACCAAACTGTACGTGCAGATCGAGGTAACAGCAGAGCATCTGGTTAACAT</th>\n",
+       "      <th>CATCCATTACCCATACAAAGTACATAATAATAAGGACAAGCACTTCATCTCCCTGTGTGACGGCTAGAGGTCAGACACTTGCAGGGCAACACCACACAAG</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -1135,10 +1234,10 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.020694</td>\n",
+       "      <td>0.010417</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>ACAGCCAATCAGGGAGTGATGCGTCCTAGGCGAAATATAACGCTCCTAAAAAAGGGGACGGGTTTCCGCCATTCTCTCTCGCTGGCATCTCTCTGGCTCT</th>\n",
+       "      <th>TGGTGGGTATTTGTGTCCACTTCCTGTCTATGCTGGGATTTTTTTCTGGTTTGAATTTGTGTTGGTCTTGTGCATGTTGCTATGTAGTCTCTGTGAGTTC</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -1159,34 +1258,82 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.025182</td>\n",
+       "      <td>0.070943</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
+       "      <th>TGACCTTGATGAGATTTTTTTTTTTCCTTCCAGGTGGGTGTAATTTTTAAATTGATAAGCATGACACAACCTTTAGACGATGCTTTAGTGCCACCTGCTG</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.061404</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GTACTTTATAATTTTTTTTCCCTCACAATAAGACTTAGAAATACATAGGTAAAACCAGAATTCCTATATTTCTGTCCTGAAAGGATCCAACTTTTAAATT</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.040351</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCATCATTTTCTACTTCTTGTTTGTGGATACAATGTGATTAAATCACTCAAGCTCCTGCCACATGGCTTCCTTGGCATGCTAGAACCTAATGTTTGACCT</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.030592</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TGTGCCGCCACACAGTGGCTTAGAGCTCAAGCTTAGGCTCTAGGTTTGGTATAACATTGAGCTGACACTATGTTGATATTCTTTAAAAAAAAACTGATAC</th>\n",
+       "      <th>GAAACGCAGCTTTTCTTGCTGAAGATCAGCAGGCCTAGGGGTGACTGGATGACTTGGGACCTGCTCGGGTGTTTCCTCCACGCTATGATCTTTTCTCCTA</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -1207,10 +1354,23 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.020250</td>\n",
+       "      <td>0.010417</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>ATGGGAGCCAGACATAGGCAGTGACATGAAAATTGCTCTCTGCAAGGGCTGGCTCAATCCAGACAGAAAAGGTTCAAAGGTGAGTCAAAGGAATCAGGGG</th>\n",
+       "      <th>TCAATCCTGAAAAGGTCTGGGTCTGACCTGTTTGGCAACACGGAGCAGATCTGCAAATTCCAGCAGCCACAGGAGGGCAGTTTTTAGGGTAACCGTGATA</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -1218,6 +1378,17 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>0.040351</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCTTCCCCCTCCCTTCTGCACCCCCCCCCACCCGGTTGCAGACGCGCCCCTTCCAGAAGCTGGGCGCGGGGGGGGGTGAGGTGGGGGCGGGAGGAGTGGA</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -1231,10 +1402,34 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.012399</td>\n",
+       "      <td>0.146930</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ATTTATCCACGTATGCCAGCTTAAGTCATTTAAGTTTGGTGGAAATCAGCATCATGCTTTCATTTTTAATGAGCCTCTGCGTACAGCAGCCACTTCTGTC</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.061404</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TTAAAGGGAGTTGGGGGAGGAGCCCATCGGTATGTCTGCAATCTGTGTCACACAACACAGCAGAGAAATGTGAGAAATACCTGAGGAATTCCACATAAAT</th>\n",
+       "      <th>ATACTGGCTCTCTATATTTTGACTTTTCTAATGTTTCAAAATAGGATCTAAAGTGAAAGTGGCAATTTCCTATGTTAGTAGCCGAAATGATATTCACCCA</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -1255,10 +1450,10 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.013701</td>\n",
+       "      <td>0.010417</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>GAAAACAGCTACAGAAGAAATTGGGCAATCCGACCCCTTGGAGGAAGTTTTTTCCCGGCGTGCTCTCTGTCGCCACCCGGTGGTGACAGAGGAGAATGCG</th>\n",
+       "      <th>TACAACAATCAAAGAGAAACTCAGAACTCCTTTGGGAGCAACAAAACCTAGGAAGAGTGAACCCTTGAAAGTGAATGTTTGGGATGCATGTAAGGCCCTT</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -1279,10 +1474,10 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.155774</td>\n",
+       "      <td>0.020614</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>CGCACAGATTTGCATAATGAGGCACACAGAGATGCAAATTTGCAGAAACTTTAAACGTGACGCTCAGATTTGCACACTGAGGCGCTCAGATGCTCTGGTG</th>\n",
+       "      <th>GCAAGATTTGTGAGCGAGGAAACGAAGTCACTGGGCACAAGGGAGGTTGCTGCACCTAAGGAACTGACTTCAAGTCCCAGCCTGGCTCTCTATTTCCCAG</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -1294,97 +1489,1108 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.231473</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.049890</td>\n",
        "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>8459 rows × 8162 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                      0    1    2    3    4  \\\n",
-       "seq                                                                           \n",
-       "ATGTAAACAGTGGAAATCAGGGACATAAAAGGAATGGTGATTGATAG...  0.0  0.0  0.0  0.0  0.0   \n",
-       "GATACCACCAGATGCTTTATGTTTTGTTGGTTGTTTAGTAATTGGCT...  0.0  0.0  0.0  0.0  0.0   \n",
-       "AAGCCATTGAGTGGTCAAAGTTCAGTGAGCTTTTCTGGGGAGCTGGA...  0.0  0.0  0.0  0.0  0.0   \n",
-       "GCTGTGGGAGGAATTGGCACAGTTTCACATGACTGGTTCATTTAAAC...  0.0  0.0  0.0  0.0  0.0   \n",
-       "ACAGCCAATCAGGGAGTGATGCGTCCTAGGCGAAATATAACGCTCCT...  0.0  0.0  0.0  0.0  0.0   \n",
-       "...                                                 ...  ...  ...  ...  ...   \n",
-       "TGTGCCGCCACACAGTGGCTTAGAGCTCAAGCTTAGGCTCTAGGTTT...  0.0  0.0  0.0  0.0  0.0   \n",
-       "ATGGGAGCCAGACATAGGCAGTGACATGAAAATTGCTCTCTGCAAGG...  0.0  0.0  0.0  0.0  0.0   \n",
-       "TTAAAGGGAGTTGGGGGAGGAGCCCATCGGTATGTCTGCAATCTGTG...  0.0  0.0  0.0  0.0  0.0   \n",
-       "GAAAACAGCTACAGAAGAAATTGGGCAATCCGACCCCTTGGAGGAAG...  0.0  0.0  0.0  0.0  0.0   \n",
-       "CGCACAGATTTGCATAATGAGGCACACAGAGATGCAAATTTGCAGAA...  0.0  0.0  0.0  0.0  0.0   \n",
+       "    <tr>\n",
+       "      <th>GCTTATCAGAAGCCTGGGCCGGTATGGAGGGCTTCCTGGGTGGGGTGGTATGGGGGGGGGGGCTGGGTAGCTGACAGACCCTTCACAGGAGAATTAATTA</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.077193</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AATCCCTGGACAACAGAGTGATTAGGGGAGAACAATGGAGACGCACCAAGCGTGGACTCAGACTGAGCCTGCTATCAGCTCTTCCTGGGTGACTTCACTC</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.020614</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GGAATCTATGTGTGTTCTTTTCAGGGAGGTCACACTGAATACATTCCTTCCTCTCCTTTGACTATTACTCATCTTTAGCTCACCTACTGGGGACAGGTAG</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.020614</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AGGTCGAAATGTGTCCAAACCCCAACAATCTCGTCCTGAGACCAGCAGGAGTGAGGACTGTATCCGCCTTGCTCTGGGGCTGCAAGTTCCTGTACTCATA</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.051645</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACAGCATGCGGCATGCTGGGTAGCATCTCCATGGCAACCGCATTGCACATCCAGAGCACTGCCAAAAACAAGTCAAGATCCAAGTAAGTAGGTGTTGC</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ATTAGATGCCATCTGATTTCAGCACTGTGTGTGTGTGTGCACACATACACACACACACACACGCACACATGCACACACACACACACGCACATGCACACAC</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CTGGAATTCCTGGAATGCCTCTCGATACAAACGAGGCATTATTCCATACCCTAAACTGTAGTCAACGATATTAACCCACCCACCTAGAAATCCCTTGCCC</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.010417</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ATCTTAACCCTTTCTAAAACACCAATGACATTGAAACTGAGCTATAAAGAGCACAGCCACTGTGGTGAAAATAAACACAATCTGCCTATAGGAGGTGTGA</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.199561</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AATTAGCCTGTCCCCAGGTCCCTGGGTGGGGCAAATGTGACTTGTCAAAGCCAGCCAGCAGAGGAAAGCAAAGCTGGTAGACAGCCCCAGTGGCTCCCAG</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.059211</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCCTGGAGCTCATGTTCAAGGCCAAATGTAGTGGTGCCTGTCTGTAGTCACAGCAAGGAGGAGGCAGAGACAGAGTCTTCGGGAAACTCACCAGCCAGCT</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.010417</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TTCAGGTCCCTCAGTCCGCGCACAGCCTGCGGGTGCACAGACCCTGATCTCACACATGCATATCAAGCTGGATCATCCCCCGCATGCCATGAAGTCACAG</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.289364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TGTGGGGAGAGCGGCTGGGATGGGAGGACTGGGGACCACTCAGACCTGAGCCACTGCAGGCAGGGTCCTCACTCAGGGTTAGGTGAGCCCCTCAGTAAAG</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.181469</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GAAAGGTGGCTGTCCTAGCTTCTTCTGTTGCTGTGACAAAACACTCTGACCAAAAGCAACATAGGAGAGGAAGGGGTTAATTCAGACTTACACCCCCAGC</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.010417</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GAGGAGGACGGGAAGCAAAGGAAAACACGTGGGTGCCAGCATTCTTTAGCAATCTGTTTCTTATCTGCAGCTAGAGGGTGACCAGTTGACTCAAGCTCTT</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.077193</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ACAAAGGGCACCATTCAGTAGCACCAGCAGGCCATACATCATCGGCTCAGAAGCCTGCCACATGACACTGCCTGCTTCTGCTGTGCCCTGCTTGGGGAGG</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.152632</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AATTAGCTCACATTTTGGTAGACTCACTTTCACCTGACAGCAATGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTATGTGTGTGTGT</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.020614</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GGACCTATGGAGGAGCAGTCAGTGCTCTACTCCTCCCTGCCTTTTTGGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTTTCTGTCTGTCGGTCTGTCCT</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.010417</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TGAACCTGTGGCCTAGCGCCACCTGCTGGTGATCATGGGGAAGTTAGGCTCTGGGCCCCTACCGTTGACCACAATGGTGAGCAGGCAGTCGAAGAGGGGT</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.175000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ACATTTGAGCCATTAAAAGTGGTCATTCTCACGTTTCCAATCCTGGGAGATCCTTCAGAAGTGACCATTTCCACATTTATTTCCTGTATTCATTCCACAC</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.010417</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CACACTGTTGACATTGTGGTTCTGAGTGTACAGTTGCCACTACTCAATGCATTTTCACACAGAGTTGTGATATTCCCAGCATCTGATGACAACCACTTTT</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.020614</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GGTAACTGGTGCTTCTTTGTTGTGGTTGGCTCTGGGCTGCTTGCTCTCCCAGGAGAGGCTGTCTGGAATGAAGAATGAGTCACATGCTCAGGTGGCTTCT</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TATATAAAGTGGAGCACCACACCTTCTGCCCCCATTGATCAATCCTGATAACTGCCCTTTTACTTCTGTGACATCAACCTGGCTAGCTTCTGGCAGGGAG</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCCCAGGTATTAATCCCGCATTTGTTACTTACTATTTACATGTGTTTCCACGCAGATTCTACTCCACATCCTGTACTGCTTTCCTCCCTTTGCCTGTCTT</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.089364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GGATACTCACCGATTAGCTAATCTGCCTGGTCAATAAACCCTTGGGGCCCATCTGTTACTTGCCCCAGACTCCAGAGCTCCTGACATTTTGAGGCGAGTG</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.030592</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CCTGTTAGCACTAGCTGCTACACATGTCCCCCATTTTCTCTCACACGAGGGGGTGCTGTGTTTCCCTGAGAGTTTATATTTTCTTCTCTGCAAATTATTG</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.010417</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GTGGTCTGCACATGCGCAGTGGCAGGCCGTGGCGGGTCTTACCTGGTACACGGGGCTTGGTGTTGCCGCCACTGAGCTGGGTTTCACGTCCACTTCCTTG</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.113048</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CTCACAAAAGACAGGCAGGAAATGTCTATGTTGTGTGTACAGTTCTTGGAAAGCACATGGAAACTGCAACACGGCATTTCCTCCAATCTCTGAAGCCTCT</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.020614</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GTCCCCTCTTCAGGCTCCGCCCCCTCTTCTGATCCCTCCCCTCTTTTGCCCCCCCCCCCCCGAATCTGTCTATGGTGCTTGCTTCACCCTCTACTGTCCA</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.030592</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GGGGGGGTTGGGCAGCTGGAGAAGGGGCTTCAGGAGGAGCCAATCCACTTCCTTCTGGCTCTGAAGTCCGCACTTATTTTCCTGGCCTGGATCACGGCTG</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.059211</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CCCTTTTCCTCTACAACCTGCAGTTACTGACTCTGACACCAGATGGCGATATAGTATGGGGAGAGAGATGGGCTCAGTTTATGCCTTTTGTACTGGAGCC</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.040351</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CAGCACTGCCTTTTGTTCTCCCAGGTTTTCAAAGCAGGGTCTTCTGTGTTCTCCTGGCTGTCCTGTAACCACCTGGCTCTGCCTCCCAAGTGCTAGGATT</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.093750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TGAGTTCTAGAGTATACAATCATCCACTTCTCTTAATGTCCACCTATTCCTTGTCTCCGACTTTTCTCATCTGTTGCCATGATGTTAAATTCAGGTGGTA</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.010417</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CATGAGTGGGAAGAGATGGTGAGCATGGAGGCTGAGGCAGGAGGCTTGGAAGTTTCACCTCTGGCTGGCAAGACGGCTCAGTGGGTAAGAGCACTGACTG</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TCGCCCAGCATTTAATCAGCTTCCTGACAGATCCCAGTAAAAGGGAGATGAATTTACGAGATTGATTAGCTAAATCCCCAAGTGCTGTGCTTACTCCGTG</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.061404</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>50 rows × 97 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                      0    1    2    3    4  \\\n",
+       "seq                                                                           \n",
+       "ACCAACAATGTCTATAAATGTTCAGATTGACAGAATGGGTAAATTTT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "AAGGCTGCCCTCATCTACCTTCCCCTCCCACATCTCAGAGACCTCAG...  0.0  0.0  0.0  0.0  0.0   \n",
+       "AGCTGCCCATCTGTTCCCTGGACTGCTGGGGTGACAGGCATGTGTTA...  0.0  0.0  0.0  0.0  0.0   \n",
+       "CTAAAGTGACAGATTAGTATATAGTTTTATAATTTGTTCAAGTCAAA...  0.0  0.0  0.0  0.0  0.0   \n",
+       "CATCCATTACCCATACAAAGTACATAATAATAAGGACAAGCACTTCA...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TGGTGGGTATTTGTGTCCACTTCCTGTCTATGCTGGGATTTTTTTCT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TGACCTTGATGAGATTTTTTTTTTTCCTTCCAGGTGGGTGTAATTTT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GTACTTTATAATTTTTTTTCCCTCACAATAAGACTTAGAAATACATA...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TCATCATTTTCTACTTCTTGTTTGTGGATACAATGTGATTAAATCAC...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GAAACGCAGCTTTTCTTGCTGAAGATCAGCAGGCCTAGGGGTGACTG...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TCAATCCTGAAAAGGTCTGGGTCTGACCTGTTTGGCAACACGGAGCA...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TCTTCCCCCTCCCTTCTGCACCCCCCCCCACCCGGTTGCAGACGCGC...  1.0  0.0  0.0  0.0  1.0   \n",
+       "ATTTATCCACGTATGCCAGCTTAAGTCATTTAAGTTTGGTGGAAATC...  0.0  0.0  0.0  0.0  0.0   \n",
+       "ATACTGGCTCTCTATATTTTGACTTTTCTAATGTTTCAAAATAGGAT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TACAACAATCAAAGAGAAACTCAGAACTCCTTTGGGAGCAACAAAAC...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GCAAGATTTGTGAGCGAGGAAACGAAGTCACTGGGCACAAGGGAGGT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GCTTATCAGAAGCCTGGGCCGGTATGGAGGGCTTCCTGGGTGGGGTG...  0.0  0.0  0.0  0.0  0.0   \n",
+       "AATCCCTGGACAACAGAGTGATTAGGGGAGAACAATGGAGACGCACC...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GGAATCTATGTGTGTTCTTTTCAGGGAGGTCACACTGAATACATTCC...  0.0  0.0  0.0  0.0  0.0   \n",
+       "AGGTCGAAATGTGTCCAAACCCCAACAATCTCGTCCTGAGACCAGCA...  0.0  0.0  0.0  0.0  0.0   \n",
+       "AAACAGCATGCGGCATGCTGGGTAGCATCTCCATGGCAACCGCATTG...  0.0  0.0  0.0  0.0  0.0   \n",
+       "ATTAGATGCCATCTGATTTCAGCACTGTGTGTGTGTGTGCACACATA...  0.0  0.0  0.0  0.0  0.0   \n",
+       "CTGGAATTCCTGGAATGCCTCTCGATACAAACGAGGCATTATTCCAT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "ATCTTAACCCTTTCTAAAACACCAATGACATTGAAACTGAGCTATAA...  2.0  0.0  0.0  0.0  0.0   \n",
+       "AATTAGCCTGTCCCCAGGTCCCTGGGTGGGGCAAATGTGACTTGTCA...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TCCTGGAGCTCATGTTCAAGGCCAAATGTAGTGGTGCCTGTCTGTAG...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TTCAGGTCCCTCAGTCCGCGCACAGCCTGCGGGTGCACAGACCCTGA...  0.0  2.0  0.0  0.0  1.0   \n",
+       "TGTGGGGAGAGCGGCTGGGATGGGAGGACTGGGGACCACTCAGACCT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GAAAGGTGGCTGTCCTAGCTTCTTCTGTTGCTGTGACAAAACACTCT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GAGGAGGACGGGAAGCAAAGGAAAACACGTGGGTGCCAGCATTCTTT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "ACAAAGGGCACCATTCAGTAGCACCAGCAGGCCATACATCATCGGCT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "AATTAGCTCACATTTTGGTAGACTCACTTTCACCTGACAGCAATGTG...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GGACCTATGGAGGAGCAGTCAGTGCTCTACTCCTCCCTGCCTTTTTG...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TGAACCTGTGGCCTAGCGCCACCTGCTGGTGATCATGGGGAAGTTAG...  1.0  0.0  0.0  0.0  0.0   \n",
+       "ACATTTGAGCCATTAAAAGTGGTCATTCTCACGTTTCCAATCCTGGG...  0.0  0.0  0.0  0.0  0.0   \n",
+       "CACACTGTTGACATTGTGGTTCTGAGTGTACAGTTGCCACTACTCAA...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GGTAACTGGTGCTTCTTTGTTGTGGTTGGCTCTGGGCTGCTTGCTCT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TATATAAAGTGGAGCACCACACCTTCTGCCCCCATTGATCAATCCTG...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TCCCAGGTATTAATCCCGCATTTGTTACTTACTATTTACATGTGTTT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GGATACTCACCGATTAGCTAATCTGCCTGGTCAATAAACCCTTGGGG...  0.0  0.0  0.0  0.0  0.0   \n",
+       "CCTGTTAGCACTAGCTGCTACACATGTCCCCCATTTTCTCTCACACG...  0.0  1.0  0.0  0.0  0.0   \n",
+       "GTGGTCTGCACATGCGCAGTGGCAGGCCGTGGCGGGTCTTACCTGGT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "CTCACAAAAGACAGGCAGGAAATGTCTATGTTGTGTGTACAGTTCTT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GTCCCCTCTTCAGGCTCCGCCCCCTCTTCTGATCCCTCCCCTCTTTT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GGGGGGGTTGGGCAGCTGGAGAAGGGGCTTCAGGAGGAGCCAATCCA...  0.0  0.0  0.0  0.0  0.0   \n",
+       "CCCTTTTCCTCTACAACCTGCAGTTACTGACTCTGACACCAGATGGC...  0.0  0.0  0.0  0.0  0.0   \n",
+       "CAGCACTGCCTTTTGTTCTCCCAGGTTTTCAAAGCAGGGTCTTCTGT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TGAGTTCTAGAGTATACAATCATCCACTTCTCTTAATGTCCACCTAT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "CATGAGTGGGAAGAGATGGTGAGCATGGAGGCTGAGGCAGGAGGCTT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TCGCCCAGCATTTAATCAGCTTCCTGACAGATCCCAGTAAAAGGGAG...  0.0  0.0  0.0  0.0  0.0   \n",
        "\n",
        "                                                      5    6    7    8    9  \\\n",
        "seq                                                                           \n",
-       "ATGTAAACAGTGGAAATCAGGGACATAAAAGGAATGGTGATTGATAG...  0.0  0.0  0.0  0.0  0.0   \n",
-       "GATACCACCAGATGCTTTATGTTTTGTTGGTTGTTTAGTAATTGGCT...  0.0  0.0  0.0  0.0  0.0   \n",
-       "AAGCCATTGAGTGGTCAAAGTTCAGTGAGCTTTTCTGGGGAGCTGGA...  0.0  0.0  0.0  0.0  0.0   \n",
-       "GCTGTGGGAGGAATTGGCACAGTTTCACATGACTGGTTCATTTAAAC...  0.0  0.0  0.0  0.0  0.0   \n",
-       "ACAGCCAATCAGGGAGTGATGCGTCCTAGGCGAAATATAACGCTCCT...  0.0  0.0  0.0  0.0  0.0   \n",
-       "...                                                 ...  ...  ...  ...  ...   \n",
-       "TGTGCCGCCACACAGTGGCTTAGAGCTCAAGCTTAGGCTCTAGGTTT...  0.0  0.0  0.0  0.0  0.0   \n",
-       "ATGGGAGCCAGACATAGGCAGTGACATGAAAATTGCTCTCTGCAAGG...  0.0  0.0  0.0  0.0  0.0   \n",
-       "TTAAAGGGAGTTGGGGGAGGAGCCCATCGGTATGTCTGCAATCTGTG...  0.0  0.0  0.0  0.0  0.0   \n",
-       "GAAAACAGCTACAGAAGAAATTGGGCAATCCGACCCCTTGGAGGAAG...  0.0  0.0  0.0  0.0  0.0   \n",
-       "CGCACAGATTTGCATAATGAGGCACACAGAGATGCAAATTTGCAGAA...  0.0  0.0  0.0  0.0  0.0   \n",
+       "ACCAACAATGTCTATAAATGTTCAGATTGACAGAATGGGTAAATTTT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "AAGGCTGCCCTCATCTACCTTCCCCTCCCACATCTCAGAGACCTCAG...  0.0  0.0  0.0  0.0  0.0   \n",
+       "AGCTGCCCATCTGTTCCCTGGACTGCTGGGGTGACAGGCATGTGTTA...  0.0  0.0  0.0  0.0  0.0   \n",
+       "CTAAAGTGACAGATTAGTATATAGTTTTATAATTTGTTCAAGTCAAA...  0.0  0.0  0.0  0.0  0.0   \n",
+       "CATCCATTACCCATACAAAGTACATAATAATAAGGACAAGCACTTCA...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TGGTGGGTATTTGTGTCCACTTCCTGTCTATGCTGGGATTTTTTTCT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TGACCTTGATGAGATTTTTTTTTTTCCTTCCAGGTGGGTGTAATTTT...  1.0  0.0  0.0  0.0  0.0   \n",
+       "GTACTTTATAATTTTTTTTCCCTCACAATAAGACTTAGAAATACATA...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TCATCATTTTCTACTTCTTGTTTGTGGATACAATGTGATTAAATCAC...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GAAACGCAGCTTTTCTTGCTGAAGATCAGCAGGCCTAGGGGTGACTG...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TCAATCCTGAAAAGGTCTGGGTCTGACCTGTTTGGCAACACGGAGCA...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TCTTCCCCCTCCCTTCTGCACCCCCCCCCACCCGGTTGCAGACGCGC...  1.0  0.0  0.0  0.0  0.0   \n",
+       "ATTTATCCACGTATGCCAGCTTAAGTCATTTAAGTTTGGTGGAAATC...  0.0  0.0  0.0  0.0  0.0   \n",
+       "ATACTGGCTCTCTATATTTTGACTTTTCTAATGTTTCAAAATAGGAT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TACAACAATCAAAGAGAAACTCAGAACTCCTTTGGGAGCAACAAAAC...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GCAAGATTTGTGAGCGAGGAAACGAAGTCACTGGGCACAAGGGAGGT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GCTTATCAGAAGCCTGGGCCGGTATGGAGGGCTTCCTGGGTGGGGTG...  0.0  0.0  0.0  0.0  0.0   \n",
+       "AATCCCTGGACAACAGAGTGATTAGGGGAGAACAATGGAGACGCACC...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GGAATCTATGTGTGTTCTTTTCAGGGAGGTCACACTGAATACATTCC...  0.0  0.0  0.0  0.0  0.0   \n",
+       "AGGTCGAAATGTGTCCAAACCCCAACAATCTCGTCCTGAGACCAGCA...  0.0  0.0  0.0  2.0  0.0   \n",
+       "AAACAGCATGCGGCATGCTGGGTAGCATCTCCATGGCAACCGCATTG...  0.0  0.0  0.0  0.0  0.0   \n",
+       "ATTAGATGCCATCTGATTTCAGCACTGTGTGTGTGTGTGCACACATA...  0.0  0.0  0.0  0.0  0.0   \n",
+       "CTGGAATTCCTGGAATGCCTCTCGATACAAACGAGGCATTATTCCAT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "ATCTTAACCCTTTCTAAAACACCAATGACATTGAAACTGAGCTATAA...  0.0  0.0  0.0  0.0  0.0   \n",
+       "AATTAGCCTGTCCCCAGGTCCCTGGGTGGGGCAAATGTGACTTGTCA...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TCCTGGAGCTCATGTTCAAGGCCAAATGTAGTGGTGCCTGTCTGTAG...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TTCAGGTCCCTCAGTCCGCGCACAGCCTGCGGGTGCACAGACCCTGA...  1.0  0.0  0.0  0.0  0.0   \n",
+       "TGTGGGGAGAGCGGCTGGGATGGGAGGACTGGGGACCACTCAGACCT...  2.0  0.0  0.0  0.0  0.0   \n",
+       "GAAAGGTGGCTGTCCTAGCTTCTTCTGTTGCTGTGACAAAACACTCT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GAGGAGGACGGGAAGCAAAGGAAAACACGTGGGTGCCAGCATTCTTT...  1.0  0.0  0.0  0.0  0.0   \n",
+       "ACAAAGGGCACCATTCAGTAGCACCAGCAGGCCATACATCATCGGCT...  1.0  0.0  0.0  1.0  0.0   \n",
+       "AATTAGCTCACATTTTGGTAGACTCACTTTCACCTGACAGCAATGTG...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GGACCTATGGAGGAGCAGTCAGTGCTCTACTCCTCCCTGCCTTTTTG...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TGAACCTGTGGCCTAGCGCCACCTGCTGGTGATCATGGGGAAGTTAG...  0.0  1.0  1.0  0.0  0.0   \n",
+       "ACATTTGAGCCATTAAAAGTGGTCATTCTCACGTTTCCAATCCTGGG...  0.0  0.0  0.0  0.0  0.0   \n",
+       "CACACTGTTGACATTGTGGTTCTGAGTGTACAGTTGCCACTACTCAA...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GGTAACTGGTGCTTCTTTGTTGTGGTTGGCTCTGGGCTGCTTGCTCT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TATATAAAGTGGAGCACCACACCTTCTGCCCCCATTGATCAATCCTG...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TCCCAGGTATTAATCCCGCATTTGTTACTTACTATTTACATGTGTTT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GGATACTCACCGATTAGCTAATCTGCCTGGTCAATAAACCCTTGGGG...  0.0  0.0  0.0  0.0  0.0   \n",
+       "CCTGTTAGCACTAGCTGCTACACATGTCCCCCATTTTCTCTCACACG...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GTGGTCTGCACATGCGCAGTGGCAGGCCGTGGCGGGTCTTACCTGGT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "CTCACAAAAGACAGGCAGGAAATGTCTATGTTGTGTGTACAGTTCTT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GTCCCCTCTTCAGGCTCCGCCCCCTCTTCTGATCCCTCCCCTCTTTT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GGGGGGGTTGGGCAGCTGGAGAAGGGGCTTCAGGAGGAGCCAATCCA...  0.0  0.0  0.0  0.0  0.0   \n",
+       "CCCTTTTCCTCTACAACCTGCAGTTACTGACTCTGACACCAGATGGC...  0.0  0.0  0.0  0.0  0.0   \n",
+       "CAGCACTGCCTTTTGTTCTCCCAGGTTTTCAAAGCAGGGTCTTCTGT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TGAGTTCTAGAGTATACAATCATCCACTTCTCTTAATGTCCACCTAT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "CATGAGTGGGAAGAGATGGTGAGCATGGAGGCTGAGGCAGGAGGCTT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TCGCCCAGCATTTAATCAGCTTCCTGACAGATCCCAGTAAAAGGGAG...  0.0  0.0  0.0  0.0  0.0   \n",
        "\n",
-       "                                                    ...  8152  8153  8154  \\\n",
-       "seq                                                 ...                     \n",
-       "ATGTAAACAGTGGAAATCAGGGACATAAAAGGAATGGTGATTGATAG...  ...   0.0   0.0   0.0   \n",
-       "GATACCACCAGATGCTTTATGTTTTGTTGGTTGTTTAGTAATTGGCT...  ...   0.0   0.0   0.0   \n",
-       "AAGCCATTGAGTGGTCAAAGTTCAGTGAGCTTTTCTGGGGAGCTGGA...  ...   0.0   0.0   0.0   \n",
-       "GCTGTGGGAGGAATTGGCACAGTTTCACATGACTGGTTCATTTAAAC...  ...   0.0   0.0   0.0   \n",
-       "ACAGCCAATCAGGGAGTGATGCGTCCTAGGCGAAATATAACGCTCCT...  ...   0.0   0.0   0.0   \n",
-       "...                                                 ...   ...   ...   ...   \n",
-       "TGTGCCGCCACACAGTGGCTTAGAGCTCAAGCTTAGGCTCTAGGTTT...  ...   0.0   0.0   0.0   \n",
-       "ATGGGAGCCAGACATAGGCAGTGACATGAAAATTGCTCTCTGCAAGG...  ...   0.0   0.0   0.0   \n",
-       "TTAAAGGGAGTTGGGGGAGGAGCCCATCGGTATGTCTGCAATCTGTG...  ...   0.0   0.0   0.0   \n",
-       "GAAAACAGCTACAGAAGAAATTGGGCAATCCGACCCCTTGGAGGAAG...  ...   0.0   0.0   0.0   \n",
-       "CGCACAGATTTGCATAATGAGGCACACAGAGATGCAAATTTGCAGAA...  ...   1.0   2.0   0.0   \n",
+       "                                                    ...   87   88   89   90  \\\n",
+       "seq                                                 ...                       \n",
+       "ACCAACAATGTCTATAAATGTTCAGATTGACAGAATGGGTAAATTTT...  ...  0.0  0.0  0.0  0.0   \n",
+       "AAGGCTGCCCTCATCTACCTTCCCCTCCCACATCTCAGAGACCTCAG...  ...  0.0  0.0  0.0  0.0   \n",
+       "AGCTGCCCATCTGTTCCCTGGACTGCTGGGGTGACAGGCATGTGTTA...  ...  0.0  0.0  0.0  0.0   \n",
+       "CTAAAGTGACAGATTAGTATATAGTTTTATAATTTGTTCAAGTCAAA...  ...  0.0  0.0  0.0  0.0   \n",
+       "CATCCATTACCCATACAAAGTACATAATAATAAGGACAAGCACTTCA...  ...  0.0  0.0  0.0  0.0   \n",
+       "TGGTGGGTATTTGTGTCCACTTCCTGTCTATGCTGGGATTTTTTTCT...  ...  0.0  0.0  0.0  0.0   \n",
+       "TGACCTTGATGAGATTTTTTTTTTTCCTTCCAGGTGGGTGTAATTTT...  ...  0.0  0.0  0.0  0.0   \n",
+       "GTACTTTATAATTTTTTTTCCCTCACAATAAGACTTAGAAATACATA...  ...  1.0  0.0  0.0  0.0   \n",
+       "TCATCATTTTCTACTTCTTGTTTGTGGATACAATGTGATTAAATCAC...  ...  0.0  0.0  0.0  0.0   \n",
+       "GAAACGCAGCTTTTCTTGCTGAAGATCAGCAGGCCTAGGGGTGACTG...  ...  0.0  0.0  0.0  0.0   \n",
+       "TCAATCCTGAAAAGGTCTGGGTCTGACCTGTTTGGCAACACGGAGCA...  ...  0.0  0.0  0.0  0.0   \n",
+       "TCTTCCCCCTCCCTTCTGCACCCCCCCCCACCCGGTTGCAGACGCGC...  ...  0.0  0.0  0.0  0.0   \n",
+       "ATTTATCCACGTATGCCAGCTTAAGTCATTTAAGTTTGGTGGAAATC...  ...  0.0  0.0  0.0  0.0   \n",
+       "ATACTGGCTCTCTATATTTTGACTTTTCTAATGTTTCAAAATAGGAT...  ...  0.0  0.0  0.0  0.0   \n",
+       "TACAACAATCAAAGAGAAACTCAGAACTCCTTTGGGAGCAACAAAAC...  ...  0.0  0.0  0.0  0.0   \n",
+       "GCAAGATTTGTGAGCGAGGAAACGAAGTCACTGGGCACAAGGGAGGT...  ...  0.0  0.0  0.0  0.0   \n",
+       "GCTTATCAGAAGCCTGGGCCGGTATGGAGGGCTTCCTGGGTGGGGTG...  ...  0.0  0.0  0.0  0.0   \n",
+       "AATCCCTGGACAACAGAGTGATTAGGGGAGAACAATGGAGACGCACC...  ...  0.0  0.0  0.0  0.0   \n",
+       "GGAATCTATGTGTGTTCTTTTCAGGGAGGTCACACTGAATACATTCC...  ...  0.0  0.0  0.0  0.0   \n",
+       "AGGTCGAAATGTGTCCAAACCCCAACAATCTCGTCCTGAGACCAGCA...  ...  0.0  0.0  0.0  0.0   \n",
+       "AAACAGCATGCGGCATGCTGGGTAGCATCTCCATGGCAACCGCATTG...  ...  0.0  0.0  0.0  0.0   \n",
+       "ATTAGATGCCATCTGATTTCAGCACTGTGTGTGTGTGTGCACACATA...  ...  0.0  0.0  0.0  0.0   \n",
+       "CTGGAATTCCTGGAATGCCTCTCGATACAAACGAGGCATTATTCCAT...  ...  0.0  0.0  0.0  0.0   \n",
+       "ATCTTAACCCTTTCTAAAACACCAATGACATTGAAACTGAGCTATAA...  ...  0.0  0.0  0.0  0.0   \n",
+       "AATTAGCCTGTCCCCAGGTCCCTGGGTGGGGCAAATGTGACTTGTCA...  ...  0.0  0.0  0.0  0.0   \n",
+       "TCCTGGAGCTCATGTTCAAGGCCAAATGTAGTGGTGCCTGTCTGTAG...  ...  0.0  0.0  0.0  0.0   \n",
+       "TTCAGGTCCCTCAGTCCGCGCACAGCCTGCGGGTGCACAGACCCTGA...  ...  0.0  0.0  0.0  0.0   \n",
+       "TGTGGGGAGAGCGGCTGGGATGGGAGGACTGGGGACCACTCAGACCT...  ...  2.0  0.0  0.0  0.0   \n",
+       "GAAAGGTGGCTGTCCTAGCTTCTTCTGTTGCTGTGACAAAACACTCT...  ...  0.0  0.0  0.0  0.0   \n",
+       "GAGGAGGACGGGAAGCAAAGGAAAACACGTGGGTGCCAGCATTCTTT...  ...  0.0  0.0  0.0  0.0   \n",
+       "ACAAAGGGCACCATTCAGTAGCACCAGCAGGCCATACATCATCGGCT...  ...  0.0  0.0  0.0  0.0   \n",
+       "AATTAGCTCACATTTTGGTAGACTCACTTTCACCTGACAGCAATGTG...  ...  0.0  0.0  0.0  0.0   \n",
+       "GGACCTATGGAGGAGCAGTCAGTGCTCTACTCCTCCCTGCCTTTTTG...  ...  0.0  0.0  0.0  0.0   \n",
+       "TGAACCTGTGGCCTAGCGCCACCTGCTGGTGATCATGGGGAAGTTAG...  ...  0.0  0.0  0.0  0.0   \n",
+       "ACATTTGAGCCATTAAAAGTGGTCATTCTCACGTTTCCAATCCTGGG...  ...  0.0  0.0  0.0  0.0   \n",
+       "CACACTGTTGACATTGTGGTTCTGAGTGTACAGTTGCCACTACTCAA...  ...  1.0  0.0  0.0  0.0   \n",
+       "GGTAACTGGTGCTTCTTTGTTGTGGTTGGCTCTGGGCTGCTTGCTCT...  ...  0.0  0.0  0.0  0.0   \n",
+       "TATATAAAGTGGAGCACCACACCTTCTGCCCCCATTGATCAATCCTG...  ...  0.0  0.0  0.0  0.0   \n",
+       "TCCCAGGTATTAATCCCGCATTTGTTACTTACTATTTACATGTGTTT...  ...  1.0  0.0  1.0  2.0   \n",
+       "GGATACTCACCGATTAGCTAATCTGCCTGGTCAATAAACCCTTGGGG...  ...  1.0  0.0  0.0  0.0   \n",
+       "CCTGTTAGCACTAGCTGCTACACATGTCCCCCATTTTCTCTCACACG...  ...  0.0  0.0  0.0  0.0   \n",
+       "GTGGTCTGCACATGCGCAGTGGCAGGCCGTGGCGGGTCTTACCTGGT...  ...  0.0  0.0  0.0  0.0   \n",
+       "CTCACAAAAGACAGGCAGGAAATGTCTATGTTGTGTGTACAGTTCTT...  ...  0.0  0.0  0.0  0.0   \n",
+       "GTCCCCTCTTCAGGCTCCGCCCCCTCTTCTGATCCCTCCCCTCTTTT...  ...  0.0  0.0  0.0  0.0   \n",
+       "GGGGGGGTTGGGCAGCTGGAGAAGGGGCTTCAGGAGGAGCCAATCCA...  ...  0.0  0.0  0.0  0.0   \n",
+       "CCCTTTTCCTCTACAACCTGCAGTTACTGACTCTGACACCAGATGGC...  ...  0.0  0.0  0.0  0.0   \n",
+       "CAGCACTGCCTTTTGTTCTCCCAGGTTTTCAAAGCAGGGTCTTCTGT...  ...  0.0  0.0  0.0  0.0   \n",
+       "TGAGTTCTAGAGTATACAATCATCCACTTCTCTTAATGTCCACCTAT...  ...  0.0  0.0  0.0  0.0   \n",
+       "CATGAGTGGGAAGAGATGGTGAGCATGGAGGCTGAGGCAGGAGGCTT...  ...  0.0  0.0  0.0  0.0   \n",
+       "TCGCCCAGCATTTAATCAGCTTCCTGACAGATCCCAGTAAAAGGGAG...  ...  0.0  0.0  0.0  0.0   \n",
        "\n",
-       "                                                    8155  8156  8157  8158  \\\n",
-       "seq                                                                          \n",
-       "ATGTAAACAGTGGAAATCAGGGACATAAAAGGAATGGTGATTGATAG...   0.0   0.0   0.0   0.0   \n",
-       "GATACCACCAGATGCTTTATGTTTTGTTGGTTGTTTAGTAATTGGCT...   0.0   0.0   0.0   0.0   \n",
-       "AAGCCATTGAGTGGTCAAAGTTCAGTGAGCTTTTCTGGGGAGCTGGA...   0.0   0.0   0.0   0.0   \n",
-       "GCTGTGGGAGGAATTGGCACAGTTTCACATGACTGGTTCATTTAAAC...   0.0   0.0   0.0   0.0   \n",
-       "ACAGCCAATCAGGGAGTGATGCGTCCTAGGCGAAATATAACGCTCCT...   0.0   0.0   0.0   0.0   \n",
-       "...                                                  ...   ...   ...   ...   \n",
-       "TGTGCCGCCACACAGTGGCTTAGAGCTCAAGCTTAGGCTCTAGGTTT...   0.0   0.0   0.0   0.0   \n",
-       "ATGGGAGCCAGACATAGGCAGTGACATGAAAATTGCTCTCTGCAAGG...   0.0   0.0   0.0   0.0   \n",
-       "TTAAAGGGAGTTGGGGGAGGAGCCCATCGGTATGTCTGCAATCTGTG...   0.0   0.0   0.0   0.0   \n",
-       "GAAAACAGCTACAGAAGAAATTGGGCAATCCGACCCCTTGGAGGAAG...   0.0   0.0   0.0   0.0   \n",
-       "CGCACAGATTTGCATAATGAGGCACACAGAGATGCAAATTTGCAGAA...   0.0   1.0   2.0   1.0   \n",
+       "                                                     91   92   93   94   95  \\\n",
+       "seq                                                                           \n",
+       "ACCAACAATGTCTATAAATGTTCAGATTGACAGAATGGGTAAATTTT...  2.0  0.0  0.0  0.0  0.0   \n",
+       "AAGGCTGCCCTCATCTACCTTCCCCTCCCACATCTCAGAGACCTCAG...  0.0  0.0  0.0  0.0  0.0   \n",
+       "AGCTGCCCATCTGTTCCCTGGACTGCTGGGGTGACAGGCATGTGTTA...  0.0  0.0  0.0  0.0  1.0   \n",
+       "CTAAAGTGACAGATTAGTATATAGTTTTATAATTTGTTCAAGTCAAA...  1.0  0.0  0.0  0.0  0.0   \n",
+       "CATCCATTACCCATACAAAGTACATAATAATAAGGACAAGCACTTCA...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TGGTGGGTATTTGTGTCCACTTCCTGTCTATGCTGGGATTTTTTTCT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TGACCTTGATGAGATTTTTTTTTTTCCTTCCAGGTGGGTGTAATTTT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GTACTTTATAATTTTTTTTCCCTCACAATAAGACTTAGAAATACATA...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TCATCATTTTCTACTTCTTGTTTGTGGATACAATGTGATTAAATCAC...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GAAACGCAGCTTTTCTTGCTGAAGATCAGCAGGCCTAGGGGTGACTG...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TCAATCCTGAAAAGGTCTGGGTCTGACCTGTTTGGCAACACGGAGCA...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TCTTCCCCCTCCCTTCTGCACCCCCCCCCACCCGGTTGCAGACGCGC...  0.0  0.0  0.0  0.0  0.0   \n",
+       "ATTTATCCACGTATGCCAGCTTAAGTCATTTAAGTTTGGTGGAAATC...  0.0  0.0  0.0  0.0  1.0   \n",
+       "ATACTGGCTCTCTATATTTTGACTTTTCTAATGTTTCAAAATAGGAT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TACAACAATCAAAGAGAAACTCAGAACTCCTTTGGGAGCAACAAAAC...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GCAAGATTTGTGAGCGAGGAAACGAAGTCACTGGGCACAAGGGAGGT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GCTTATCAGAAGCCTGGGCCGGTATGGAGGGCTTCCTGGGTGGGGTG...  0.0  1.0  0.0  0.0  0.0   \n",
+       "AATCCCTGGACAACAGAGTGATTAGGGGAGAACAATGGAGACGCACC...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GGAATCTATGTGTGTTCTTTTCAGGGAGGTCACACTGAATACATTCC...  0.0  0.0  0.0  0.0  0.0   \n",
+       "AGGTCGAAATGTGTCCAAACCCCAACAATCTCGTCCTGAGACCAGCA...  0.0  0.0  0.0  0.0  0.0   \n",
+       "AAACAGCATGCGGCATGCTGGGTAGCATCTCCATGGCAACCGCATTG...  0.0  0.0  0.0  0.0  0.0   \n",
+       "ATTAGATGCCATCTGATTTCAGCACTGTGTGTGTGTGTGCACACATA...  0.0  0.0  0.0  0.0  0.0   \n",
+       "CTGGAATTCCTGGAATGCCTCTCGATACAAACGAGGCATTATTCCAT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "ATCTTAACCCTTTCTAAAACACCAATGACATTGAAACTGAGCTATAA...  0.0  0.0  0.0  0.0  0.0   \n",
+       "AATTAGCCTGTCCCCAGGTCCCTGGGTGGGGCAAATGTGACTTGTCA...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TCCTGGAGCTCATGTTCAAGGCCAAATGTAGTGGTGCCTGTCTGTAG...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TTCAGGTCCCTCAGTCCGCGCACAGCCTGCGGGTGCACAGACCCTGA...  0.0  1.0  0.0  0.0  0.0   \n",
+       "TGTGGGGAGAGCGGCTGGGATGGGAGGACTGGGGACCACTCAGACCT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GAAAGGTGGCTGTCCTAGCTTCTTCTGTTGCTGTGACAAAACACTCT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GAGGAGGACGGGAAGCAAAGGAAAACACGTGGGTGCCAGCATTCTTT...  0.0  0.0  1.0  0.0  0.0   \n",
+       "ACAAAGGGCACCATTCAGTAGCACCAGCAGGCCATACATCATCGGCT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "AATTAGCTCACATTTTGGTAGACTCACTTTCACCTGACAGCAATGTG...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GGACCTATGGAGGAGCAGTCAGTGCTCTACTCCTCCCTGCCTTTTTG...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TGAACCTGTGGCCTAGCGCCACCTGCTGGTGATCATGGGGAAGTTAG...  0.0  0.0  0.0  1.0  0.0   \n",
+       "ACATTTGAGCCATTAAAAGTGGTCATTCTCACGTTTCCAATCCTGGG...  0.0  0.0  0.0  0.0  0.0   \n",
+       "CACACTGTTGACATTGTGGTTCTGAGTGTACAGTTGCCACTACTCAA...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GGTAACTGGTGCTTCTTTGTTGTGGTTGGCTCTGGGCTGCTTGCTCT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TATATAAAGTGGAGCACCACACCTTCTGCCCCCATTGATCAATCCTG...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TCCCAGGTATTAATCCCGCATTTGTTACTTACTATTTACATGTGTTT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GGATACTCACCGATTAGCTAATCTGCCTGGTCAATAAACCCTTGGGG...  0.0  0.0  0.0  0.0  1.0   \n",
+       "CCTGTTAGCACTAGCTGCTACACATGTCCCCCATTTTCTCTCACACG...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GTGGTCTGCACATGCGCAGTGGCAGGCCGTGGCGGGTCTTACCTGGT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "CTCACAAAAGACAGGCAGGAAATGTCTATGTTGTGTGTACAGTTCTT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GTCCCCTCTTCAGGCTCCGCCCCCTCTTCTGATCCCTCCCCTCTTTT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "GGGGGGGTTGGGCAGCTGGAGAAGGGGCTTCAGGAGGAGCCAATCCA...  0.0  0.0  0.0  0.0  0.0   \n",
+       "CCCTTTTCCTCTACAACCTGCAGTTACTGACTCTGACACCAGATGGC...  0.0  0.0  0.0  1.0  0.0   \n",
+       "CAGCACTGCCTTTTGTTCTCCCAGGTTTTCAAAGCAGGGTCTTCTGT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TGAGTTCTAGAGTATACAATCATCCACTTCTCTTAATGTCCACCTAT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "CATGAGTGGGAAGAGATGGTGAGCATGGAGGCTGAGGCAGGAGGCTT...  0.0  0.0  0.0  0.0  0.0   \n",
+       "TCGCCCAGCATTTAATCAGCTTCCTGACAGATCCCAGTAAAAGGGAG...  0.0  0.0  0.0  0.0  0.0   \n",
        "\n",
-       "                                                    8159  8160       var  \n",
-       "seq                                                                       \n",
-       "ATGTAAACAGTGGAAATCAGGGACATAAAAGGAATGGTGATTGATAG...   0.0   0.0  0.006698  \n",
-       "GATACCACCAGATGCTTTATGTTTTGTTGGTTGTTTAGTAATTGGCT...   0.0   0.0  0.022447  \n",
-       "AAGCCATTGAGTGGTCAAAGTTCAGTGAGCTTTTCTGGGGAGCTGGA...   0.0   0.0  0.015358  \n",
-       "GCTGTGGGAGGAATTGGCACAGTTTCACATGACTGGTTCATTTAAAC...   0.0   0.0  0.020694  \n",
-       "ACAGCCAATCAGGGAGTGATGCGTCCTAGGCGAAATATAACGCTCCT...   0.0   0.0  0.025182  \n",
-       "...                                                  ...   ...       ...  \n",
-       "TGTGCCGCCACACAGTGGCTTAGAGCTCAAGCTTAGGCTCTAGGTTT...   0.0   0.0  0.020250  \n",
-       "ATGGGAGCCAGACATAGGCAGTGACATGAAAATTGCTCTCTGCAAGG...   0.0   0.0  0.012399  \n",
-       "TTAAAGGGAGTTGGGGGAGGAGCCCATCGGTATGTCTGCAATCTGTG...   0.0   0.0  0.013701  \n",
-       "GAAAACAGCTACAGAAGAAATTGGGCAATCCGACCCCTTGGAGGAAG...   0.0   0.0  0.155774  \n",
-       "CGCACAGATTTGCATAATGAGGCACACAGAGATGCAAATTTGCAGAA...   0.0   0.0  0.231473  \n",
+       "                                                         var  \n",
+       "seq                                                           \n",
+       "ACCAACAATGTCTATAAATGTTCAGATTGACAGAATGGGTAAATTTT...  0.061404  \n",
+       "AAGGCTGCCCTCATCTACCTTCCCCTCCCACATCTCAGAGACCTCAG...  0.030592  \n",
+       "AGCTGCCCATCTGTTCCCTGGACTGCTGGGGTGACAGGCATGTGTTA...  0.030592  \n",
+       "CTAAAGTGACAGATTAGTATATAGTTTTATAATTTGTTCAAGTCAAA...  0.115351  \n",
+       "CATCCATTACCCATACAAAGTACATAATAATAAGGACAAGCACTTCA...  0.010417  \n",
+       "TGGTGGGTATTTGTGTCCACTTCCTGTCTATGCTGGGATTTTTTTCT...  0.070943  \n",
+       "TGACCTTGATGAGATTTTTTTTTTTCCTTCCAGGTGGGTGTAATTTT...  0.061404  \n",
+       "GTACTTTATAATTTTTTTTCCCTCACAATAAGACTTAGAAATACATA...  0.040351  \n",
+       "TCATCATTTTCTACTTCTTGTTTGTGGATACAATGTGATTAAATCAC...  0.030592  \n",
+       "GAAACGCAGCTTTTCTTGCTGAAGATCAGCAGGCCTAGGGGTGACTG...  0.010417  \n",
+       "TCAATCCTGAAAAGGTCTGGGTCTGACCTGTTTGGCAACACGGAGCA...  0.040351  \n",
+       "TCTTCCCCCTCCCTTCTGCACCCCCCCCCACCCGGTTGCAGACGCGC...  0.146930  \n",
+       "ATTTATCCACGTATGCCAGCTTAAGTCATTTAAGTTTGGTGGAAATC...  0.061404  \n",
+       "ATACTGGCTCTCTATATTTTGACTTTTCTAATGTTTCAAAATAGGAT...  0.010417  \n",
+       "TACAACAATCAAAGAGAAACTCAGAACTCCTTTGGGAGCAACAAAAC...  0.020614  \n",
+       "GCAAGATTTGTGAGCGAGGAAACGAAGTCACTGGGCACAAGGGAGGT...  0.049890  \n",
+       "GCTTATCAGAAGCCTGGGCCGGTATGGAGGGCTTCCTGGGTGGGGTG...  0.077193  \n",
+       "AATCCCTGGACAACAGAGTGATTAGGGGAGAACAATGGAGACGCACC...  0.020614  \n",
+       "GGAATCTATGTGTGTTCTTTTCAGGGAGGTCACACTGAATACATTCC...  0.020614  \n",
+       "AGGTCGAAATGTGTCCAAACCCCAACAATCTCGTCCTGAGACCAGCA...  0.051645  \n",
+       "AAACAGCATGCGGCATGCTGGGTAGCATCTCCATGGCAACCGCATTG...  0.000000  \n",
+       "ATTAGATGCCATCTGATTTCAGCACTGTGTGTGTGTGTGCACACATA...  0.000000  \n",
+       "CTGGAATTCCTGGAATGCCTCTCGATACAAACGAGGCATTATTCCAT...  0.010417  \n",
+       "ATCTTAACCCTTTCTAAAACACCAATGACATTGAAACTGAGCTATAA...  0.199561  \n",
+       "AATTAGCCTGTCCCCAGGTCCCTGGGTGGGGCAAATGTGACTTGTCA...  0.059211  \n",
+       "TCCTGGAGCTCATGTTCAAGGCCAAATGTAGTGGTGCCTGTCTGTAG...  0.010417  \n",
+       "TTCAGGTCCCTCAGTCCGCGCACAGCCTGCGGGTGCACAGACCCTGA...  0.289364  \n",
+       "TGTGGGGAGAGCGGCTGGGATGGGAGGACTGGGGACCACTCAGACCT...  0.181469  \n",
+       "GAAAGGTGGCTGTCCTAGCTTCTTCTGTTGCTGTGACAAAACACTCT...  0.010417  \n",
+       "GAGGAGGACGGGAAGCAAAGGAAAACACGTGGGTGCCAGCATTCTTT...  0.077193  \n",
+       "ACAAAGGGCACCATTCAGTAGCACCAGCAGGCCATACATCATCGGCT...  0.152632  \n",
+       "AATTAGCTCACATTTTGGTAGACTCACTTTCACCTGACAGCAATGTG...  0.020614  \n",
+       "GGACCTATGGAGGAGCAGTCAGTGCTCTACTCCTCCCTGCCTTTTTG...  0.010417  \n",
+       "TGAACCTGTGGCCTAGCGCCACCTGCTGGTGATCATGGGGAAGTTAG...  0.175000  \n",
+       "ACATTTGAGCCATTAAAAGTGGTCATTCTCACGTTTCCAATCCTGGG...  0.010417  \n",
+       "CACACTGTTGACATTGTGGTTCTGAGTGTACAGTTGCCACTACTCAA...  0.020614  \n",
+       "GGTAACTGGTGCTTCTTTGTTGTGGTTGGCTCTGGGCTGCTTGCTCT...  0.000000  \n",
+       "TATATAAAGTGGAGCACCACACCTTCTGCCCCCATTGATCAATCCTG...  0.000000  \n",
+       "TCCCAGGTATTAATCCCGCATTTGTTACTTACTATTTACATGTGTTT...  0.089364  \n",
+       "GGATACTCACCGATTAGCTAATCTGCCTGGTCAATAAACCCTTGGGG...  0.030592  \n",
+       "CCTGTTAGCACTAGCTGCTACACATGTCCCCCATTTTCTCTCACACG...  0.010417  \n",
+       "GTGGTCTGCACATGCGCAGTGGCAGGCCGTGGCGGGTCTTACCTGGT...  0.113048  \n",
+       "CTCACAAAAGACAGGCAGGAAATGTCTATGTTGTGTGTACAGTTCTT...  0.020614  \n",
+       "GTCCCCTCTTCAGGCTCCGCCCCCTCTTCTGATCCCTCCCCTCTTTT...  0.030592  \n",
+       "GGGGGGGTTGGGCAGCTGGAGAAGGGGCTTCAGGAGGAGCCAATCCA...  0.059211  \n",
+       "CCCTTTTCCTCTACAACCTGCAGTTACTGACTCTGACACCAGATGGC...  0.040351  \n",
+       "CAGCACTGCCTTTTGTTCTCCCAGGTTTTCAAAGCAGGGTCTTCTGT...  0.093750  \n",
+       "TGAGTTCTAGAGTATACAATCATCCACTTCTCTTAATGTCCACCTAT...  0.010417  \n",
+       "CATGAGTGGGAAGAGATGGTGAGCATGGAGGCTGAGGCAGGAGGCTT...  0.000000  \n",
+       "TCGCCCAGCATTTAATCAGCTTCCTGACAGATCCCAGTAAAAGGGAG...  0.061404  \n",
        "\n",
-       "[8459 rows x 8162 columns]"
+       "[50 rows x 97 columns]"
       ]
      },
-     "execution_count": 111,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1395,7 +2601,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 41,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -1410,7 +2616,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1419,7 +2625,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 43,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -1430,10 +2636,10 @@
     {
      "data": {
       "text/plain": [
-       "(8459, 8162)"
+       "(50, 97)"
       ]
      },
-     "execution_count": 114,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1447,7 +2653,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 44,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -1463,7 +2669,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 45,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -1474,10 +2680,10 @@
     {
      "data": {
       "text/plain": [
-       "(8459, 8161)"
+       "(45, 96)"
       ]
      },
-     "execution_count": 116,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1498,16 +2704,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "((8161, 8459), (8459, 8161))"
+       "((96, 50), (45, 96))"
       ]
      },
-     "execution_count": 117,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1518,16 +2724,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(8161, 8459)"
+       "(96, 50)"
       ]
      },
-     "execution_count": 118,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1538,16 +2744,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(8161, 8459)"
+       "(96, 50)"
       ]
      },
-     "execution_count": 119,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1558,7 +2764,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1567,7 +2773,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -1584,7 +2790,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 51,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -1622,7 +2828,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 52,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -1660,7 +2866,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 53,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -1682,7 +2888,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 54,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -1698,7 +2904,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 55,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -1712,7 +2918,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 56,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -1726,7 +2932,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1736,7 +2942,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 58,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -1753,7 +2959,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 59,
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -1785,7 +2991,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
@@ -1794,7 +3000,7 @@
        "286"
       ]
      },
-     "execution_count": 131,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1805,7 +3011,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1816,193 +3022,392 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((96, 14663), (96, 50))"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rna_sample.shape, ad.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(96, 96)\n",
+      "setting up log dynamic\n",
+      "no err\n"
+     ]
+    }
+   ],
+   "source": [
+    "criterion = mb.tl.PoissonLoss()\n",
+    "w = [r.shape[1] for r in pwms]\n",
+    "optimize_log_dynamic = True\n",
+    "model = mb.models.Mubind.make_model(train, n_kernels, criterion, kernels=[0, 2,] + w, # [0, 2] + w,\n",
+    "                                        # use_dinuc=True, dinuc_mode='full',\n",
+    "                                        optimize_sym_weight=False,\n",
+    "                                        optimize_exp_barrier=True,\n",
+    "                                        optimize_prob_act=True,\n",
+    "                                        optimize_log_dynamic=optimize_log_dynamic,\n",
+    "                                        use_dinuc=False,\n",
+    "                                        device=device,\n",
+    "                                        p_dropout=0.8,\n",
+    "                                        prepare_knn=optimize_log_dynamic,\n",
+    "                                        velocity_graph=vgraph,\n",
+    "                                        knn_free_weights=False,\n",
+    "                                        adata=None if not optimize_log_dynamic else ad,\n",
+    "                                        dinuc_mode=None)\n",
+    "print('no err')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i, batch in enumerate(train):\n",
+    "    # print(i, 'batches out of', n_batches)\n",
+    "    # Get a batch and potentially send it to GPU memory.\n",
+    "    mononuc = batch[\"mononuc\"] # .to(self.device)\n",
+    "    # print(mononuc.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "((8161, 14663), (8161, 8459))"
-      ]
-     },
-     "execution_count": 133,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rna_sample.shape, ad.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "metadata": {},
-   "outputs": [
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "setting up log dynamic\n",
+      "break\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1350x270 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "verbose=2\n",
+      "current kernel 0\n",
+      "\n",
+      "### next filter to optimize 0 (intercept)\n",
+      "\n",
+      "FREEZING KERNELS\n",
+      "optimizing feature type mono\n",
+      "next kernels 0-69, n=70\n",
+      "filters mask None\n",
+      "optimizer:  Adam , criterion: PoissonLoss \n",
+      "epochs: 15 \n",
+      "early_stopping: 4\n",
+      "lr= 0.01, weight_decay= 0, dir weight= 0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:00<00:00, 32.98it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current time: 2024-08-23 13:57:11.473182\n",
+      "\tLoss: 491.383 , R2: 0.053\n",
+      "\tTraining time (model/function): (0.457s / 0.457s)\n",
+      "\t\tper epoch (model/function): (0.033s/ 0.033s)\n",
+      "\t\tper 1k samples: 0.725s\n",
+      "('etas corr with lib_sizes (before refinement)', SignificanceResult(statistic=0.9855554970132433, pvalue=2.6722945125527147e-74))\n",
+      "optimization of dinuc is not valid for the intercept (filter=0). Skip...\n",
+      "current kernel 70\n",
+      "\n",
+      "### next filter to optimize 70 \n",
+      "\n",
+      "FREEZING KERNELS\n",
+      "optimizing feature type mono\n",
+      "next kernels 70-139, n=70\n",
+      "filters mask None\n",
+      "optimizer:  Adam , criterion: PoissonLoss \n",
+      "epochs: 15 \n",
+      "early_stopping: 4\n",
+      "lr= 0.01, weight_decay= 0, dir weight= 0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:00<00:00, 32.78it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current time: 2024-08-23 13:57:11.975469\n",
+      "\tLoss: 482.796 , R2: 0.053\n",
+      "\tTraining time (model/function): (0.916s / 0.459s)\n",
+      "\t\tper epoch (model/function): (0.065s/ 0.033s)\n",
+      "\t\tper 1k samples: 0.729s\n",
+      "('etas corr with lib_sizes (before refinement)', SignificanceResult(statistic=0.9855554970132433, pvalue=2.6722945125527147e-74))\n",
+      "\n",
+      "\n",
+      "final refinement step (after shift)...unfreezing all layers\n",
+      "optimizer:  Adam , criterion: PoissonLoss \n",
+      "epochs: 15 \n",
+      "early_stopping: 4\n",
+      "lr= 0.01, weight_decay= 0, dir weight= 0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:00<00:00, 32.32it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current time: 2024-08-23 13:57:12.484066\n",
+      "\tLoss: 245.507 , R2: 0.053\n",
+      "\tTraining time (model/function): (1.382s / 0.466s)\n",
+      "\t\tper epoch (model/function): (0.099s/ 0.033s)\n",
+      "\t\tper 1k samples: 0.740s\n",
+      "best loss 245.507\n",
+      "last five r2 values, by sequential filter optimization: ['0.053']\n",
+      "('etas corr with lib_sizes (before refinement)', SignificanceResult(statistic=0.9855554970132433, pvalue=2.6722945125527147e-74))\n",
+      "optimizing feature type dinuc\n",
+      "the optimization of dinucleotide features is skipped...\n",
+      "current kernel 140\n",
+      "\n",
+      "### next filter to optimize 140 \n",
+      "\n",
+      "FREEZING KERNELS\n",
+      "optimizing feature type mono\n",
+      "next kernels 140-209, n=70\n",
+      "filters mask None\n",
+      "optimizer:  Adam , criterion: PoissonLoss \n",
+      "epochs: 15 \n",
+      "early_stopping: 4\n",
+      "lr= 0.01, weight_decay= 0, dir weight= 0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:00<00:00, 33.20it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current time: 2024-08-23 13:57:13.013861\n",
+      "\tLoss: 463.878 , R2: 0.053\n",
+      "\tTraining time (model/function): (1.835s / 0.453s)\n",
+      "\t\tper epoch (model/function): (0.131s/ 0.032s)\n",
+      "\t\tper 1k samples: 0.720s\n",
+      "('etas corr with lib_sizes (before refinement)', SignificanceResult(statistic=0.9855554970132433, pvalue=2.6722945125527147e-74))\n",
+      "\n",
+      "\n",
+      "final refinement step (after shift)...unfreezing all layers\n",
+      "optimizer:  Adam , criterion: PoissonLoss \n",
+      "epochs: 15 \n",
+      "early_stopping: 4\n",
+      "lr= 0.01, weight_decay= 0, dir weight= 0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:00<00:00, 32.31it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current time: 2024-08-23 13:57:13.522058\n",
+      "\tLoss: 226.356 , R2: 0.053\n",
+      "\tTraining time (model/function): (2.301s / 0.466s)\n",
+      "\t\tper epoch (model/function): (0.164s/ 0.033s)\n",
+      "\t\tper 1k samples: 0.739s\n",
+      "best loss 226.356\n",
+      "last five r2 values, by sequential filter optimization: ['0.053', '0.053']\n",
+      "('etas corr with lib_sizes (before refinement)', SignificanceResult(statistic=0.9855554970132433, pvalue=2.6722945125527147e-74))\n",
+      "optimizing feature type dinuc\n",
+      "the optimization of dinucleotide features is skipped...\n",
+      "current kernel 210\n",
+      "\n",
+      "### next filter to optimize 210 \n",
+      "\n",
+      "FREEZING KERNELS\n",
+      "optimizing feature type mono\n",
+      "next kernels 210-279, n=70\n",
+      "filters mask None\n",
+      "optimizer:  Adam , criterion: PoissonLoss \n",
+      "epochs: 15 \n",
+      "early_stopping: 4\n",
+      "lr= 0.01, weight_decay= 0, dir weight= 0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:00<00:00, 32.60it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current time: 2024-08-23 13:57:14.060022\n",
+      "\tLoss: 445.175 , R2: 0.053\n",
+      "\tTraining time (model/function): (2.762s / 0.461s)\n",
+      "\t\tper epoch (model/function): (0.197s/ 0.033s)\n",
+      "\t\tper 1k samples: 0.733s\n",
+      "('etas corr with lib_sizes (before refinement)', SignificanceResult(statistic=0.9855554970132433, pvalue=2.6722945125527147e-74))\n",
+      "\n",
+      "\n",
+      "final refinement step (after shift)...unfreezing all layers\n",
+      "optimizer:  Adam , criterion: PoissonLoss \n",
+      "epochs: 15 \n",
+      "early_stopping: 4\n",
+      "lr= 0.01, weight_decay= 0, dir weight= 0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:00<00:00, 32.49it/s]\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(8161, 8161)\n",
-      "setting up log dynamic\n",
-      "no err\n"
+      "Current time: 2024-08-23 13:57:14.565799\n",
+      "\tLoss: 208.614 , R2: 0.053\n",
+      "\tTraining time (model/function): (3.225s / 0.463s)\n",
+      "\t\tper epoch (model/function): (0.230s/ 0.033s)\n",
+      "\t\tper 1k samples: 0.735s\n",
+      "best loss 208.614\n",
+      "last five r2 values, by sequential filter optimization: ['0.053', '0.053', '0.053']\n",
+      "('etas corr with lib_sizes (before refinement)', SignificanceResult(statistic=0.9855554970132433, pvalue=2.6722945125527147e-74))\n",
+      "optimizing feature type dinuc\n",
+      "the optimization of dinucleotide features is skipped...\n",
+      "current kernel 280\n",
+      "\n",
+      "### next filter to optimize 280 \n",
+      "\n",
+      "FREEZING KERNELS\n",
+      "optimizing feature type mono\n",
+      "next kernels 280-349, n=70\n",
+      "filters mask None\n",
+      "optimizer:  Adam , criterion: PoissonLoss \n",
+      "epochs: 15 \n",
+      "early_stopping: 4\n",
+      "lr= 0.01, weight_decay= 0, dir weight= 0\n"
      ]
-    }
-   ],
-   "source": [
-    "criterion = mb.tl.PoissonLoss()\n",
-    "w = [r.shape[1] for r in pwms]\n",
-    "optimize_log_dynamic = True\n",
-    "model = mb.models.Mubind.make_model(train, n_kernels, criterion, kernels=[0, 2,] + w, # [0, 2] + w,\n",
-    "                                        # use_dinuc=True, dinuc_mode='full',\n",
-    "                                        optimize_sym_weight=False,\n",
-    "                                        optimize_exp_barrier=True,\n",
-    "                                        optimize_prob_act=True,\n",
-    "                                        optimize_log_dynamic=optimize_log_dynamic,\n",
-    "                                        use_dinuc=False,\n",
-    "                                        device=device,\n",
-    "                                        p_dropout=0.8,\n",
-    "                                        prepare_knn=optimize_log_dynamic,\n",
-    "                                        velocity_graph=vgraph,\n",
-    "                                        knn_free_weights=False,\n",
-    "                                        adata=None if not optimize_log_dynamic else ad,\n",
-    "                                        dinuc_mode=None)\n",
-    "print('no err')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:00<00:00, 33.05it/s]\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([128, 4, 100])\n",
-      "torch.Size([11, 4, 100])\n"
+      "Current time: 2024-08-23 13:57:15.097639\n",
+      "\tLoss: 428.723 , R2: 0.053\n",
+      "\tTraining time (model/function): (3.681s / 0.455s)\n",
+      "\t\tper epoch (model/function): (0.263s/ 0.033s)\n",
+      "\t\tper 1k samples: 0.723s\n",
+      "('etas corr with lib_sizes (before refinement)', SignificanceResult(statistic=0.9855554970132433, pvalue=2.6722945125527147e-74))\n",
+      "\n",
+      "\n",
+      "final refinement step (after shift)...unfreezing all layers\n",
+      "optimizer:  Adam , criterion: PoissonLoss \n",
+      "epochs: 15 \n",
+      "early_stopping: 4\n",
+      "lr= 0.01, weight_decay= 0, dir weight= 0\n"
      ]
-    }
-   ],
-   "source": [
-    "for i, batch in enumerate(train):\n",
-    "    # print(i, 'batches out of', n_batches)\n",
-    "    # Get a batch and potentially send it to GPU memory.\n",
-    "    mononuc = batch[\"mononuc\"] # .to(self.device)\n",
-    "    print(mononuc.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 138,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
+    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[autoreload of mubind.models.models failed: Traceback (most recent call last):\n",
-      "  File \"/home/ilibarra/miniconda3/envs/mubind/lib/python3.9/site-packages/IPython/extensions/autoreload.py\", line 276, in check\n",
-      "    superreload(m, reload, self.old_objects)\n",
-      "  File \"/home/ilibarra/miniconda3/envs/mubind/lib/python3.9/site-packages/IPython/extensions/autoreload.py\", line 475, in superreload\n",
-      "    module = reload(module)\n",
-      "  File \"/home/ilibarra/miniconda3/envs/mubind/lib/python3.9/importlib/__init__.py\", line 169, in reload\n",
-      "    _bootstrap._exec(spec, module)\n",
-      "  File \"<frozen importlib._bootstrap>\", line 613, in _exec\n",
-      "  File \"<frozen importlib._bootstrap_external>\", line 846, in exec_module\n",
-      "  File \"<frozen importlib._bootstrap_external>\", line 983, in get_code\n",
-      "  File \"<frozen importlib._bootstrap_external>\", line 913, in source_to_code\n",
-      "  File \"<frozen importlib._bootstrap>\", line 228, in _call_with_frames_removed\n",
-      "  File \"/home/ilibarra/workspace/theislab/mubind/mubind/models/models.py\", line 1581\n",
-      "    out_mono = self.conv_mono[i](mono)\n",
-      "    ^\n",
-      "SyntaxError: invalid syntax\n",
-      "]\n"
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:00<00:00, 32.84it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "setting up log dynamic\n",
-      "order None\n",
-      "0 out of 288...\n",
+      "Current time: 2024-08-23 13:57:15.598558\n",
+      "\tLoss: 193.628 , R2: 0.054\n",
+      "\tTraining time (model/function): (4.139s / 0.458s)\n",
+      "\t\tper epoch (model/function): (0.296s/ 0.033s)\n",
+      "\t\tper 1k samples: 0.727s\n",
+      "best loss 193.628\n",
+      "last five r2 values, by sequential filter optimization: ['0.053', '0.053', '0.053', '0.054']\n",
+      "('etas corr with lib_sizes (before refinement)', SignificanceResult(statistic=0.9855554970132433, pvalue=2.6722945125527147e-74))\n",
+      "optimizing feature type dinuc\n",
+      "the optimization of dinucleotide features is skipped...\n",
+      "\n",
+      "optimization finished:\n",
+      "total time: 4.139026641845703s\n",
+      "Time per epoch (total): 0.001 s\n",
       "break\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1350x270 with 9 Axes>"
       ]
@@ -2033,5200 +3438,268 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "  0%|                                                                                                                                                                                                         | 0/15 [00:00<?, ?it/s]"
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:00<00:00, 36.77it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "1 Conv1d(4, 1, kernel_size=(2,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "2 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "3 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "4 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "5 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "6 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "7 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "8 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "9 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "10 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "11 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "12 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "13 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "14 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "15 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "16 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "17 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "18 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "19 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "20 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "21 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "22 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "23 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "24 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "25 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "26 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "27 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "28 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "29 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "30 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "31 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "32 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "33 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "34 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "35 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "36 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "37 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "38 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "39 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "40 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "41 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "42 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "43 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "44 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "45 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "46 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "47 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "48 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "49 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "50 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "51 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "52 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "53 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "54 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "55 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "56 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "57 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "58 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "59 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "60 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "61 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "62 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "63 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "64 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "65 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "66 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "67 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "68 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "69 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "70 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "71 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "72 Conv1d(4, 1, kernel_size=(6,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "73 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "74 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "75 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "76 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "77 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "78 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "79 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "80 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "81 Conv1d(4, 1, kernel_size=(21,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "82 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "83 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "84 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "85 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "86 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "87 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "88 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "89 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "90 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "91 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "92 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "93 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "94 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "95 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "96 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "97 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "98 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "99 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "100 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "101 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "102 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "103 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "104 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "105 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "106 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "107 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "108 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "109 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "110 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "111 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "112 Conv1d(4, 1, kernel_size=(7,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "113 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "114 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "115 Conv1d(4, 1, kernel_size=(7,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "116 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "117 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "118 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "119 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "120 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "121 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "122 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "123 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "124 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "125 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "126 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "127 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "128 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "129 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "130 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "131 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "132 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "133 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "134 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "135 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "136 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "137 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "138 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "139 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "140 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "141 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "142 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "143 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "144 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "145 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "146 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "147 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "148 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "149 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "150 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "151 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "152 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "153 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "154 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "155 Conv1d(4, 1, kernel_size=(7,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "156 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "157 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "158 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "159 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "160 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "161 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "162 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "163 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "164 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "165 Conv1d(4, 1, kernel_size=(21,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "166 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "167 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "168 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "169 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "170 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "171 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "172 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "173 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "174 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "175 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "176 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "177 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "178 Conv1d(4, 1, kernel_size=(6,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "179 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "180 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "181 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "182 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "183 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "184 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "185 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "186 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "187 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "188 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "189 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "190 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "191 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "192 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "193 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "194 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "195 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "196 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "197 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "198 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "199 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "200 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "201 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "202 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "203 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "204 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "205 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "206 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "207 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "208 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "209 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "210 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "211 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "212 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "213 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "214 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "215 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "216 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "217 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "218 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "219 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "220 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "221 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "222 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "223 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "224 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "225 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "226 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "227 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "228 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "229 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "230 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "231 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "232 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "233 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "234 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "235 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "236 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "237 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "238 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "239 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "240 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "241 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "242 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "243 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "244 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "245 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "246 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "247 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "248 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "249 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "250 Conv1d(4, 1, kernel_size=(23,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "251 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "252 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "253 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "254 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "255 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "256 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "257 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "258 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "259 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "260 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "261 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "262 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "263 Conv1d(4, 1, kernel_size=(23,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "264 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "265 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "266 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "267 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "268 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "269 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "270 Conv1d(4, 1, kernel_size=(6,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "271 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "272 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "273 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "274 Conv1d(4, 1, kernel_size=(21,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "275 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "276 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "277 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "278 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "279 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "280 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "281 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "282 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "283 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "284 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "285 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "286 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "287 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "1 Conv1d(4, 1, kernel_size=(2,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "2 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "3 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "4 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "5 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "6 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "7 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "8 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "9 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "10 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "11 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "12 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "13 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "14 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "15 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "16 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "17 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "18 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "19 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "20 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "21 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "22 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "23 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "24 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "25 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "26 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "27 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "28 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "29 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "30 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "31 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "32 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "33 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "34 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "35 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "36 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "37 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "38 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "39 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "40 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "41 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "42 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "43 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "44 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "45 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "46 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "47 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "48 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "49 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "50 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "51 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "52 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "53 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "54 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "55 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "56 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "57 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "58 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "59 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "60 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "61 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "62 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "63 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "64 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "65 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "66 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "67 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "68 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "69 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "70 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "71 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "72 Conv1d(4, 1, kernel_size=(6,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "73 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "74 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "75 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "76 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "77 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "78 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "79 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "80 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "81 Conv1d(4, 1, kernel_size=(21,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "82 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "83 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "84 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "85 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "86 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "87 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "88 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "89 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "90 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "91 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "92 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "93 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "94 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "95 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "96 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "97 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "98 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "99 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "100 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "101 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "102 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "103 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "104 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "105 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "106 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "107 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "108 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "109 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "110 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "111 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "112 Conv1d(4, 1, kernel_size=(7,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "113 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "114 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "115 Conv1d(4, 1, kernel_size=(7,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "116 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "117 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "118 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "119 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "120 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "121 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "122 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "123 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "124 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "125 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "126 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "127 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "128 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "129 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "130 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "131 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "132 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "133 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "134 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "135 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "136 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "137 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "138 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "139 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "140 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "141 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "142 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "143 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "144 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "145 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "146 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "147 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "148 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "149 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "150 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "151 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "152 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "153 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "154 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "155 Conv1d(4, 1, kernel_size=(7,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "156 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "157 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "158 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "159 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "160 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "161 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "162 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "163 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "164 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "165 Conv1d(4, 1, kernel_size=(21,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "166 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "167 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "168 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "169 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "170 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "171 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "172 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "173 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "174 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "175 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "176 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "177 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "178 Conv1d(4, 1, kernel_size=(6,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "179 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "180 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "181 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "182 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "183 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "184 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "185 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "186 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "187 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "188 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "189 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "190 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "191 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "192 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "193 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "194 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "195 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "196 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "197 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "198 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "199 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "200 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "201 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "202 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "203 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "204 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "205 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "206 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "207 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "208 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "209 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "210 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "211 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "212 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "213 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "214 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "215 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "216 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "217 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "218 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "219 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "220 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "221 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "222 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "223 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "224 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "225 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "226 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "227 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "228 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "229 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "230 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "231 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "232 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "233 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "234 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "235 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "236 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "237 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "238 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "239 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "240 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "241 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "242 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "243 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "244 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "245 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "246 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "247 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "248 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "249 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "250 Conv1d(4, 1, kernel_size=(23,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "251 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "252 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "253 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "254 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "255 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "256 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "257 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "258 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "259 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "260 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "261 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "262 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "263 Conv1d(4, 1, kernel_size=(23,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "264 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "265 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "266 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "267 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "268 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "269 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "270 Conv1d(4, 1, kernel_size=(6,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "271 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "272 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "273 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "274 Conv1d(4, 1, kernel_size=(21,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "275 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "276 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "277 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "278 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "279 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "280 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "281 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "282 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "283 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "284 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "285 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "286 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "287 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "1 Conv1d(4, 1, kernel_size=(2,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "2 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "3 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "4 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "5 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "6 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "7 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "8 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "9 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "10 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "11 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "12 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "13 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "14 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "15 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "16 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "17 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "18 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "19 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "20 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "21 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "22 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "23 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "24 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "25 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "26 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "27 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "28 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "29 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "30 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "31 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "32 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "33 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "34 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "35 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "36 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "37 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "38 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "39 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "40 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "41 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "42 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "43 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "44 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "45 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "46 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "47 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "48 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "49 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "50 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "51 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "52 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "53 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "54 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "55 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "56 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "57 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "58 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "59 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "60 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "61 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "62 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "63 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "64 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "65 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "66 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "67 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "68 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "69 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "70 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "71 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "72 Conv1d(4, 1, kernel_size=(6,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "73 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "74 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "75 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "76 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "77 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "78 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "79 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "80 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "81 Conv1d(4, 1, kernel_size=(21,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "82 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "83 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "84 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "85 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "86 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "87 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "88 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "89 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "90 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "91 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "92 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "93 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "94 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "95 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "96 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "97 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "98 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "99 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "100 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "101 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "102 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "103 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "104 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "105 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "106 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "107 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "108 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "109 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "110 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "111 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "112 Conv1d(4, 1, kernel_size=(7,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "113 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "114 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "115 Conv1d(4, 1, kernel_size=(7,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "116 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "117 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "118 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "119 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "120 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "121 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "122 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "123 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "124 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "125 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "126 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "127 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "128 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "129 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "130 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "131 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "132 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "133 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "134 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "135 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "136 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "137 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "138 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "139 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "140 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "141 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "142 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "143 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "144 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "145 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "146 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "147 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "148 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "149 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "150 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "151 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "152 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "153 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "154 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "155 Conv1d(4, 1, kernel_size=(7,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "156 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "157 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "158 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "159 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "160 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "161 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "162 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "163 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "164 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "165 Conv1d(4, 1, kernel_size=(21,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "166 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "167 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "168 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "169 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "170 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "171 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "172 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "173 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "174 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "175 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "176 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "177 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "178 Conv1d(4, 1, kernel_size=(6,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "179 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "180 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "181 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "182 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "183 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "184 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "185 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "186 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "187 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "188 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "189 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "190 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "191 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "192 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "193 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "194 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "195 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "196 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "197 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "198 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "199 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "200 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "201 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "202 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "203 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "204 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "205 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "206 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "207 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "208 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "209 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "210 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "211 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "212 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "213 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "214 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "215 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "216 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "217 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "218 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "219 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "220 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "221 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "222 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "223 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "224 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "225 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "226 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "227 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "228 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "229 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "230 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "231 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "232 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "233 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "234 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "235 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "236 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "237 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "238 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "239 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "240 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "241 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "242 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "243 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "244 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "245 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "246 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "247 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "248 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "249 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "250 Conv1d(4, 1, kernel_size=(23,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "251 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "252 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "253 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "254 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "255 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "256 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "257 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "258 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "259 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "260 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "261 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "262 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "263 Conv1d(4, 1, kernel_size=(23,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "264 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "265 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "266 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "267 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "268 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "269 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "270 Conv1d(4, 1, kernel_size=(6,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "271 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "272 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "273 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "274 Conv1d(4, 1, kernel_size=(21,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "275 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "276 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "277 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "278 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "279 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "280 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "281 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "282 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "283 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "284 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "285 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "286 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "287 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "1 Conv1d(4, 1, kernel_size=(2,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "2 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "3 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "4 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "5 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "6 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "7 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "8 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "9 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "10 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "11 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "12 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "13 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "14 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "15 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "16 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "17 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "18 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "19 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "20 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "21 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "22 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "23 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "24 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "25 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "26 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "27 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "28 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "29 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "30 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "31 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "32 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "33 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "34 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "35 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "36 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "37 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "38 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "39 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "40 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "41 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "42 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "43 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "44 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "45 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "46 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "47 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "48 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "49 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "50 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "51 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "52 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "53 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "54 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "55 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "56 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "57 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "58 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "59 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "60 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "61 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "62 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "63 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "64 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "65 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "66 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "67 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "68 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "69 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "70 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "71 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "72 Conv1d(4, 1, kernel_size=(6,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "73 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "74 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "75 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "76 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "77 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "78 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "79 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "80 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "81 Conv1d(4, 1, kernel_size=(21,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "82 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "83 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "84 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "85 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "86 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "87 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "88 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "89 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "90 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "91 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "92 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "93 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "94 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "95 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "96 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "97 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "98 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "99 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "100 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "101 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "102 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "103 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "104 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "105 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "106 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "107 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "108 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "109 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "110 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "111 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "112 Conv1d(4, 1, kernel_size=(7,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "113 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "114 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "115 Conv1d(4, 1, kernel_size=(7,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "116 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "117 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "118 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "119 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "120 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "121 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "122 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "123 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "124 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "125 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "126 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "127 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "128 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "129 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "130 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "131 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "132 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "133 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "134 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "135 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "136 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "137 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "138 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "139 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "140 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "141 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "142 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "143 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "144 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "145 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "146 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "147 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "148 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "149 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "150 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "151 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "152 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "153 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "154 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "155 Conv1d(4, 1, kernel_size=(7,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "156 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "157 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "158 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "159 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "160 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "161 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "162 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "163 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "164 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "165 Conv1d(4, 1, kernel_size=(21,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "166 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "167 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "168 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "169 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "170 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "171 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "172 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "173 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "174 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "175 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "176 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "177 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "178 Conv1d(4, 1, kernel_size=(6,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "179 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "180 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "181 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "182 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "183 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "184 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "185 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "186 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "187 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "188 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "189 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "190 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "191 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "192 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "193 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "194 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "195 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "196 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "197 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "198 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "199 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "200 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "201 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "202 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "203 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "204 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "205 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "206 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "207 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "208 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "209 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "210 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "211 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "212 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "213 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "214 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "215 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "216 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "217 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "218 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "219 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "220 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "221 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "222 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "223 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "224 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "225 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "226 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "227 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "228 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "229 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "230 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "231 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "232 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "233 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "234 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "235 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "236 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "237 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "238 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "239 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "240 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "241 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "242 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "243 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "244 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "245 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "246 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "247 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "248 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "249 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "250 Conv1d(4, 1, kernel_size=(23,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "251 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "252 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "253 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "254 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "255 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "256 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "257 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "258 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "259 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "260 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "261 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "262 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "263 Conv1d(4, 1, kernel_size=(23,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "264 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "265 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "266 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "267 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "268 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "269 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "270 Conv1d(4, 1, kernel_size=(6,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "271 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "272 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "273 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "274 Conv1d(4, 1, kernel_size=(21,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "275 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "276 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "277 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "278 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "279 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "280 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "281 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "282 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "283 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "284 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "285 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "286 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "287 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "1 Conv1d(4, 1, kernel_size=(2,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "2 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "3 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "4 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "5 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "6 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "7 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "8 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "9 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "10 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "11 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "12 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "13 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "14 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "15 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "16 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "17 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "18 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "19 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "20 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "21 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "22 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "23 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "24 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "25 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "26 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "27 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "28 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "29 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "30 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "31 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "32 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "33 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "34 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "35 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "36 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "37 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "38 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "39 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "40 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "41 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "42 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "43 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "44 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "45 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "46 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "47 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "48 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "49 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "50 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "51 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "52 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "53 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "54 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "55 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "56 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "57 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "58 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "59 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "60 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "61 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "62 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "63 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "64 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "65 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "66 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "67 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "68 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "69 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "70 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "71 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "72 Conv1d(4, 1, kernel_size=(6,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "73 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "74 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "75 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "76 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "77 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "78 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "79 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "80 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "81 Conv1d(4, 1, kernel_size=(21,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "82 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "83 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "84 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "85 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "86 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "87 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "88 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "89 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "90 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "91 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "92 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "93 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "94 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "95 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "96 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "97 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "98 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "99 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "100 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "101 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "102 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "103 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "104 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "105 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "106 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "107 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "108 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "109 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "110 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "111 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "112 Conv1d(4, 1, kernel_size=(7,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "113 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "114 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "115 Conv1d(4, 1, kernel_size=(7,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "116 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "117 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "118 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "119 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "120 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "121 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "122 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "123 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "124 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "125 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "126 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "127 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "128 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "129 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "130 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "131 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "132 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "133 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "134 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "135 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "136 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "137 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "138 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "139 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "140 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "141 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "142 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "143 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "144 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "145 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "146 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "147 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "148 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "149 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "150 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "151 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "152 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "153 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "154 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "155 Conv1d(4, 1, kernel_size=(7,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "156 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "157 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "158 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "159 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "160 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "161 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "162 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "163 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "164 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "165 Conv1d(4, 1, kernel_size=(21,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "166 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "167 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "168 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "169 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "170 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "171 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "172 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "173 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "174 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "175 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "176 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "177 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "178 Conv1d(4, 1, kernel_size=(6,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "179 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "180 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "181 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "182 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "183 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "184 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "185 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "186 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "187 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "188 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "189 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "190 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "191 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "192 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "193 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "194 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "195 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "196 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "197 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "198 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "199 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "200 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "201 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "202 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "203 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "204 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "205 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "206 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "207 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "208 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "209 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "210 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "211 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "212 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "213 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "214 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "215 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "216 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "217 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "218 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "219 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "220 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "221 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "222 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "223 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "224 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "225 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "226 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "227 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "228 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "229 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "230 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "231 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "232 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "233 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "234 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "235 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "236 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "237 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "238 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "239 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "240 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "241 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "242 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "243 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "244 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "245 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "246 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "247 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "248 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "249 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "250 Conv1d(4, 1, kernel_size=(23,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "251 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "252 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "253 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "254 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "255 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "256 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "257 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "258 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "259 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "260 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "261 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "262 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "263 Conv1d(4, 1, kernel_size=(23,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "264 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "265 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "266 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "267 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "268 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "269 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "270 Conv1d(4, 1, kernel_size=(6,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "271 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "272 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "273 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "274 Conv1d(4, 1, kernel_size=(21,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "275 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "276 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "277 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "278 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "279 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "280 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "281 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "282 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "283 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "284 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "285 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "286 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "287 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "1 Conv1d(4, 1, kernel_size=(2,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "2 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "3 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "4 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "5 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "6 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "7 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "8 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "9 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "10 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "11 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "12 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "13 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "14 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "15 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "16 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "17 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "18 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "19 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "20 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "21 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "22 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "23 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "24 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "25 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "26 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "27 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "28 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "29 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "30 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "31 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "32 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "33 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "34 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "35 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "36 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "37 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "38 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "39 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "40 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "41 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "42 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "43 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "44 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "45 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "46 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "47 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "48 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "49 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "50 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "51 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "52 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "53 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "54 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "55 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "56 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "57 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "58 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "59 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "60 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "61 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "62 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "63 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "64 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "65 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "66 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "67 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "68 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "69 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "70 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "71 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "72 Conv1d(4, 1, kernel_size=(6,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "73 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "74 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "75 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "76 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "77 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "78 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "79 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "80 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "81 Conv1d(4, 1, kernel_size=(21,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "82 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "83 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "84 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "85 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "86 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "87 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "88 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "89 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "90 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "91 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "92 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "93 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "94 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "95 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "96 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "97 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "98 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "99 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "100 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "101 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "102 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "103 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "104 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "105 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "106 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "107 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "108 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "109 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "110 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "111 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "112 Conv1d(4, 1, kernel_size=(7,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "113 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "114 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "115 Conv1d(4, 1, kernel_size=(7,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "116 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "117 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "118 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "119 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "120 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "121 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "122 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "123 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "124 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "125 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "126 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "127 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "128 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "129 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "130 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "131 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "132 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "133 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "134 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "135 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "136 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "137 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "138 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "139 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "140 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "141 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "142 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "143 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "144 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "145 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "146 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "147 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "148 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "149 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "150 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "151 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "152 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "153 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "154 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "155 Conv1d(4, 1, kernel_size=(7,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "156 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "157 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "158 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "159 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "160 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "161 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "162 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "163 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "164 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "165 Conv1d(4, 1, kernel_size=(21,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "166 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "167 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "168 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "169 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "170 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "171 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "172 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "173 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "174 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "175 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "176 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "177 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "178 Conv1d(4, 1, kernel_size=(6,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "179 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "180 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "181 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "182 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "183 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "184 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "185 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "186 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "187 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "188 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "189 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "190 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "191 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "192 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "193 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "194 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "195 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "196 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "197 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "198 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "199 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "200 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "201 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "202 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "203 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "204 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "205 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "206 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "207 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "208 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "209 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "210 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "211 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "212 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "213 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "214 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "215 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "216 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "217 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "218 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "219 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "220 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "221 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "222 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "223 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "224 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "225 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "226 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "227 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "228 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "229 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "230 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "231 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "232 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "233 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "234 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "235 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "236 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "237 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "238 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "239 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "240 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "241 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "242 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "243 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "244 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "245 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "246 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "247 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "248 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "249 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "250 Conv1d(4, 1, kernel_size=(23,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "251 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "252 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "253 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "254 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "255 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "256 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "257 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "258 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "259 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "260 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "261 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "262 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "263 Conv1d(4, 1, kernel_size=(23,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "264 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "265 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "266 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "267 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "268 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "269 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "270 Conv1d(4, 1, kernel_size=(6,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "271 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "272 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "273 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "274 Conv1d(4, 1, kernel_size=(21,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "275 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "276 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "277 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "278 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "279 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "280 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "281 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "282 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "283 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "284 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "285 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "286 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "287 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "1 Conv1d(4, 1, kernel_size=(2,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "2 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "3 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "4 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "5 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "6 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "7 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "8 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "9 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "10 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "11 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "12 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "13 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "14 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "15 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "16 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "17 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "18 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "19 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "20 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "21 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "22 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "23 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "24 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "25 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "26 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "27 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "28 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "29 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "30 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "31 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "32 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "33 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "34 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "35 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "36 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "37 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "38 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "39 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "40 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "41 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "42 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "43 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "44 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "45 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "46 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "47 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "48 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "49 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "50 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "51 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "52 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "53 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "54 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "55 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "56 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "57 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "58 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "59 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "60 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "61 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "62 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "63 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "64 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "65 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "66 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "67 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "68 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "69 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "70 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "71 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "72 Conv1d(4, 1, kernel_size=(6,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "73 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "74 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "75 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "76 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "77 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "78 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "79 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "80 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "81 Conv1d(4, 1, kernel_size=(21,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "82 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "83 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "84 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "85 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "86 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "87 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "88 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "89 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "90 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "91 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "92 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "93 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "94 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "95 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "96 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "97 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "98 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "99 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "100 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "101 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "102 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "103 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "104 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "105 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "106 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "107 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "108 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "109 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "110 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "111 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "112 Conv1d(4, 1, kernel_size=(7,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "113 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "114 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "115 Conv1d(4, 1, kernel_size=(7,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "116 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "117 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "118 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "119 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "120 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "121 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "122 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "123 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "124 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "125 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "126 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "127 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "128 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "129 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "130 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "131 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "132 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "133 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "134 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "135 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "136 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "137 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "138 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "139 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "140 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "141 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "142 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "143 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "144 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "145 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "146 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "147 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "148 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "149 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "150 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "151 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "152 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "153 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "154 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "155 Conv1d(4, 1, kernel_size=(7,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "156 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "157 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "158 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "159 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "160 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "161 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "162 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "163 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "164 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "165 Conv1d(4, 1, kernel_size=(21,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "166 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "167 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "168 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "169 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "170 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "171 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "172 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "173 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "174 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "175 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "176 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "177 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "178 Conv1d(4, 1, kernel_size=(6,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "179 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "180 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "181 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "182 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "183 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "184 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "185 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "186 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "187 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "188 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "189 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "190 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "191 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "192 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "193 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "194 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "195 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "196 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "197 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "198 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "199 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "200 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "201 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "202 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "203 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "204 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "205 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "206 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "207 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "208 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "209 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "210 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "211 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "212 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "213 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "214 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "215 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "216 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "217 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "218 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "219 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "220 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "221 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "222 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "223 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "224 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "225 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "226 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "227 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "228 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "229 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "230 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "231 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "232 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "233 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "234 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "235 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "236 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "237 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "238 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "239 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "240 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "241 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "242 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "243 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "244 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "245 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "246 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "247 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "248 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "249 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "250 Conv1d(4, 1, kernel_size=(23,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "251 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "252 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "253 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "254 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "255 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "256 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "257 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "258 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "259 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "260 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "261 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "262 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "263 Conv1d(4, 1, kernel_size=(23,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "264 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "265 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "266 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "267 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "268 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "269 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "270 Conv1d(4, 1, kernel_size=(6,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "271 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "272 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "273 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "274 Conv1d(4, 1, kernel_size=(21,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "275 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "276 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "277 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "278 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "279 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "280 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "281 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "282 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "283 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "284 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "285 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "286 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "287 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "1 Conv1d(4, 1, kernel_size=(2,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "2 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "3 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "4 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "5 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "6 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "7 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "8 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "9 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "10 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "11 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "12 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "13 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "14 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "15 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "16 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "17 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "18 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "19 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "20 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "21 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "22 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "23 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "24 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "25 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "26 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "27 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "28 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "29 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "30 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "31 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "32 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "33 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "34 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "35 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "36 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "37 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "38 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "39 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "40 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "41 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "42 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "43 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "44 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "45 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "46 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "47 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "48 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "49 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "50 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "51 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "52 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "53 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "54 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "55 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "56 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "57 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "58 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "59 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "60 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "61 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "62 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "63 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "64 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "65 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "66 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "67 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "68 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "69 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "70 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "71 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "72 Conv1d(4, 1, kernel_size=(6,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "73 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "74 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "75 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "76 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "77 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "78 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "79 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "80 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "81 Conv1d(4, 1, kernel_size=(21,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "82 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "83 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "84 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "85 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "86 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "87 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "88 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "89 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "90 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "91 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "92 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "93 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "94 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "95 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "96 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "97 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "98 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "99 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "100 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "101 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "102 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "103 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "104 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "105 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "106 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "107 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "108 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "109 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "110 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "111 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "112 Conv1d(4, 1, kernel_size=(7,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "113 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "114 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "115 Conv1d(4, 1, kernel_size=(7,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "116 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "117 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "118 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "119 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "120 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "121 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "122 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "123 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "124 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "125 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "126 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "127 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "128 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "129 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "130 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "131 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "132 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "133 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "134 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "135 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "136 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "137 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "138 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "139 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "140 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "141 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "142 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "143 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "144 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "145 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "146 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "147 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "148 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "149 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "150 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "151 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "152 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "153 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "154 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "155 Conv1d(4, 1, kernel_size=(7,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "156 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "157 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "158 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "159 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "160 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "161 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "162 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "163 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "164 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "165 Conv1d(4, 1, kernel_size=(21,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "166 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "167 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "168 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "169 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "170 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "171 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "172 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "173 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "174 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "175 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "176 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "177 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "178 Conv1d(4, 1, kernel_size=(6,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "179 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "180 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "181 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "182 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "183 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "184 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "185 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "186 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "187 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "188 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "189 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "190 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "191 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "192 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "193 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "194 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "195 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "196 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "197 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "198 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "199 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "200 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "201 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "202 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "203 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "204 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "205 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "206 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "207 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "208 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "209 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "210 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "211 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "212 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "213 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "214 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "215 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "216 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "217 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "218 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "219 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "220 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "221 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "222 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "223 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "224 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "225 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "226 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "227 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "228 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "229 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "230 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "231 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "232 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "233 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "234 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "235 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "236 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "237 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "238 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "239 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "240 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "241 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "242 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "243 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "244 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "245 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "246 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "247 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "248 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "249 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "250 Conv1d(4, 1, kernel_size=(23,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "251 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "252 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "253 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "254 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "255 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "256 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "257 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "258 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "259 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "260 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "261 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "262 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "263 Conv1d(4, 1, kernel_size=(23,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "264 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "265 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "266 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "267 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "268 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "269 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "270 Conv1d(4, 1, kernel_size=(6,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "271 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "272 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "273 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "274 Conv1d(4, 1, kernel_size=(21,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "275 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "276 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "277 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "278 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "279 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "280 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "281 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "282 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "283 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "284 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "285 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "286 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "287 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "1 Conv1d(4, 1, kernel_size=(2,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "2 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "3 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "4 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "5 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "6 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "7 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "8 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "9 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "10 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "11 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "12 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "13 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "14 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "15 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "16 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "17 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "18 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "19 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "20 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "21 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "22 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "23 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "24 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "25 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "26 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "27 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "28 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "29 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "30 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "31 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "32 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "33 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "34 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "35 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "36 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "37 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "38 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "39 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "40 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "41 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "42 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "43 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "44 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "45 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "46 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "47 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "48 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "49 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "50 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "51 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "52 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "53 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "54 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "55 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "56 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "57 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "58 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "59 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "60 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "61 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "62 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "63 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "64 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "65 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "66 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "67 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "68 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "69 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "70 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "71 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "72 Conv1d(4, 1, kernel_size=(6,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "73 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "74 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "75 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "76 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "77 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "78 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "79 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "80 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "81 Conv1d(4, 1, kernel_size=(21,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "82 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "83 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "84 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "85 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "86 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "87 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "88 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "89 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "90 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "91 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "92 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "93 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "94 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "95 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "96 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "97 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "98 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "99 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "100 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "101 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "102 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "103 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "104 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "105 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "106 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "107 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "108 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "109 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "110 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "111 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "112 Conv1d(4, 1, kernel_size=(7,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "113 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "114 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "115 Conv1d(4, 1, kernel_size=(7,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "116 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "117 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "118 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "119 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "120 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "121 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "122 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "123 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "124 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "125 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "126 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "127 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "128 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "129 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "130 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "131 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "132 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "133 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "134 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "135 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "136 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "137 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "138 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "139 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "140 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "141 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "142 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "143 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "144 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "145 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "146 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "147 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "148 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "149 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "150 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "151 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "152 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "153 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "154 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "155 Conv1d(4, 1, kernel_size=(7,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "156 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "157 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "158 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "159 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "160 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "161 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "162 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "163 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "164 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "165 Conv1d(4, 1, kernel_size=(21,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "166 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "167 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "168 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "169 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "170 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "171 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "172 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "173 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "174 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "175 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "176 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "177 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "178 Conv1d(4, 1, kernel_size=(6,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "179 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "180 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "181 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "182 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "183 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "184 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "185 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "186 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "187 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "188 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "189 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "190 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "191 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "192 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "193 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "194 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "195 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "196 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "197 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "198 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "199 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "200 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "201 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "202 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "203 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "204 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "205 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "206 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "207 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "208 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "209 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "210 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "211 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "212 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "213 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "214 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "215 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "216 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "217 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "218 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "219 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "220 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "221 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "222 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "223 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "224 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "225 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "226 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "227 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "228 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "229 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "230 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "231 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "232 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "233 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "234 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "235 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "236 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "237 Conv1d(4, 1, kernel_size=(16,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "238 Conv1d(4, 1, kernel_size=(8,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "239 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "240 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "241 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "242 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "243 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "244 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "245 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "246 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "247 Conv1d(4, 1, kernel_size=(14,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "248 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "249 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "250 Conv1d(4, 1, kernel_size=(23,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "251 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "252 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "253 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "254 Conv1d(4, 1, kernel_size=(9,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "255 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "256 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "257 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "258 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "259 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "260 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "261 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "262 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "263 Conv1d(4, 1, kernel_size=(23,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "264 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "265 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "266 Conv1d(4, 1, kernel_size=(11,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "267 Conv1d(4, 1, kernel_size=(24,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "268 Conv1d(4, 1, kernel_size=(12,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "269 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "270 Conv1d(4, 1, kernel_size=(6,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "271 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "272 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "273 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "274 Conv1d(4, 1, kernel_size=(21,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "275 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "276 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "277 Conv1d(4, 1, kernel_size=(13,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "278 Conv1d(4, 1, kernel_size=(19,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "279 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "280 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "281 Conv1d(4, 1, kernel_size=(17,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "282 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "283 Conv1d(4, 1, kernel_size=(10,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "284 Conv1d(4, 1, kernel_size=(20,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "285 Conv1d(4, 1, kernel_size=(22,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "286 Conv1d(4, 1, kernel_size=(18,), stride=(1,), bias=False)\n",
-      "here torch.Size([128, 4, 146]) torch.Size([128, 4, 146])\n",
-      "287 Conv1d(4, 1, kernel_size=(15,), stride=(1,), bias=False)\n"
+      "Current time: 2024-08-23 13:57:17.298718\n",
+      "\tLoss: 491.360 , R2: 0.057\n",
+      "\tTraining time (model/function): (0.409s / 0.409s)\n",
+      "\t\tper epoch (model/function): (0.029s/ 0.029s)\n",
+      "\t\tper 1k samples: 0.650s\n",
+      "('etas corr with lib_sizes (before refinement)', SignificanceResult(statistic=0.9855756636883769, pvalue=2.5036006799198427e-74))\n",
+      "optimization of dinuc is not valid for the intercept (filter=0). Skip...\n",
+      "current kernel 70\n",
+      "\n",
+      "### next filter to optimize 70 \n",
+      "\n",
+      "FREEZING KERNELS\n",
+      "optimizing feature type mono\n",
+      "next kernels 70-139, n=70\n",
+      "filters mask None\n",
+      "optimizer:  Adam , criterion: PoissonLoss \n",
+      "epochs: 15 \n",
+      "early_stopping: 4\n",
+      "lr= 0.01, weight_decay= 0, dir weight= 0\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "  0%|                                                                                                                                                                                                         | 0/15 [00:08<?, ?it/s]\n"
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:00<00:00, 36.88it/s]\n"
      ]
     },
     {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[138], line 61\u001b[0m\n\u001b[1;32m     58\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnn\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mtnn\u001b[39;00m\n\u001b[1;32m     60\u001b[0m model\u001b[38;5;241m.\u001b[39mgraph_module\u001b[38;5;241m.\u001b[39muse_hadamard \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m---> 61\u001b[0m model, best_loss \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimize_iterative\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     62\u001b[0m \u001b[43m                                            \u001b[49m\u001b[43mn_epochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mn_epochs_intercept\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mn_epochs_kernel\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_kernels\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     63\u001b[0m \u001b[43m                                            \u001b[49m\u001b[43mshow_logo\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m     64\u001b[0m \u001b[43m                                            \u001b[49m\u001b[43muse_mono\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_dinuc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdinuc_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlocal\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     65\u001b[0m \u001b[43m                                            \u001b[49m\u001b[43mopt_kernel_shift\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_kernels\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     66\u001b[0m \u001b[43m                                            \u001b[49m\u001b[43mopt_kernel_length\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_kernels\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     67\u001b[0m \u001b[43m                                            \u001b[49m\u001b[43mopt_one_step\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m     68\u001b[0m \u001b[43m                                            \u001b[49m\u001b[43mshift_max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshift_step\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     69\u001b[0m \u001b[43m                                            \u001b[49m\u001b[38;5;66;43;03m# optimiser=topti.SGD,\u001b[39;49;00m\n\u001b[1;32m     70\u001b[0m \u001b[43m                                            \u001b[49m\u001b[43moptimiser\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtopti\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mAdam\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     71\u001b[0m \u001b[43m                                            \u001b[49m\u001b[43mrel_chg_early_stop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m5e-3\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     72\u001b[0m \u001b[43m                                            \u001b[49m\u001b[43mn_unfreeze_kernels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_unfreeze_kernels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[43m                                            \u001b[49m\u001b[38;5;66;43;03m# skip_kernels=list(range(1, 2)) + list(range(5, 500)),\u001b[39;49;00m\n\u001b[1;32m     74\u001b[0m \u001b[43m                                            \u001b[49m\u001b[43mn_batches\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_rounds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_epochs_shift_factor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# log_etas=log_etas, # log_etas=log_etas,\u001b[39;49;00m\n\u001b[1;32m     75\u001b[0m \u001b[43m                                            \u001b[49m\u001b[43mkernels\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m20\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_kernels\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mr2_per_epoch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m     76\u001b[0m \u001b[43m                                            \u001b[49m\u001b[43mexp_max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m8\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     77\u001b[0m \u001b[43m                                            \u001b[49m\u001b[43mearly_stopping\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlog_each\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlog_each\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mw\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m20\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_w\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m20\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m#  target_dim=train.dataset.signal.shape[1])\u001b[39;00m\n\u001b[1;32m     79\u001b[0m model_by_logdynamic[optimize_log_dynamic] \u001b[38;5;241m=\u001b[39m model\n",
-      "File \u001b[0;32m~/workspace/theislab/mubind/mubind/models/models.py:985\u001b[0m, in \u001b[0;36mMubind.optimize_iterative\u001b[0;34m(self, train, max_w, n_epochs, early_stopping, log_each, opt_kernel_shift, opt_kernel_length, opt_one_step, expand_length_max, expand_length_step, show_logo, optimiser, seed, init_random, joint_learning, ignore_kernel, n_unfreeze_kernels, lr, weight_decay, stop_at_kernel, dirichlet_regularization, verbose, exp_max, shift_max, shift_step, r2_per_epoch, skip_kernels, log_next_r2, **kwargs)\u001b[0m\n\u001b[1;32m    982\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m verbose \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    983\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfilters mask\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_ignore_kernel())\n\u001b[0;32m--> 985\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimize_simple\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    986\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    987\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnext_optimiser\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    988\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnum_epochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_epochs\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mn_epochs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mn_epochs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    989\u001b[0m \u001b[43m    \u001b[49m\u001b[43mearly_stopping\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnext_early_stopping\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    990\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlog_each\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlog_each\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    991\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdirichlet_regularization\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdirichlet_regularization\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    992\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexp_max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexp_max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    993\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    994\u001b[0m \u001b[43m    \u001b[49m\u001b[43mr2_per_epoch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mr2_per_epoch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    995\u001b[0m \u001b[43m    \u001b[49m\u001b[43mi\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    996\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    997\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    999\u001b[0m \u001b[38;5;66;03m# vprint('grad')\u001b[39;00m\n\u001b[1;32m   1000\u001b[0m \u001b[38;5;66;03m# vprint(model.binding_modes.conv_mono[1].weight.grad)\u001b[39;00m\n\u001b[1;32m   1001\u001b[0m \u001b[38;5;66;03m# vprint(model.binding_modes.conv_di[1].weight.grad)\u001b[39;00m\n\u001b[1;32m   1002\u001b[0m \u001b[38;5;66;03m# vprint('')\u001b[39;00m\n\u001b[1;32m   1004\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloss_color \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(np\u001b[38;5;241m.\u001b[39mrepeat(colors[i \u001b[38;5;241m%\u001b[39m \u001b[38;5;28mlen\u001b[39m(colors)], \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloss_history) \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloss_color)))\n",
-      "File \u001b[0;32m~/workspace/theislab/mubind/mubind/models/models.py:707\u001b[0m, in \u001b[0;36mMubind.optimize_simple\u001b[0;34m(self, dataloader, optimiser, num_epochs, early_stopping, dirichlet_regularization, exp_max, log_each, verbose, r2_per_epoch, **kwargs)\u001b[0m\n\u001b[1;32m    705\u001b[0m loss_neg_weights \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloss_exp_barrier(exp_max\u001b[38;5;241m=\u001b[39mexp_max)\n\u001b[1;32m    706\u001b[0m loss_sym_weights \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloss_kernel_symmetrical_weights()\n\u001b[0;32m--> 707\u001b[0m loss_log_dynamic \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloss_log_dynamic\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    709\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptimize_kernel_rel:\n\u001b[1;32m    710\u001b[0m     loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m loss_kernel_rel\n",
-      "File \u001b[0;32m~/workspace/theislab/mubind/mubind/models/models.py:461\u001b[0m, in \u001b[0;36mMubind.loss_log_dynamic\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    455\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m u_idx \u001b[38;5;129;01min\u001b[39;00m uniq_idx:\n\u001b[1;32m    456\u001b[0m     \u001b[38;5;66;03m# at least one common index has to be present in the position retrieved\u001b[39;00m\n\u001b[1;32m    457\u001b[0m \n\u001b[1;32m    458\u001b[0m     \u001b[38;5;66;03m# print(pos.device, idx.device)\u001b[39;00m\n\u001b[1;32m    460\u001b[0m     sub_pos \u001b[38;5;241m=\u001b[39m pos[(idx[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m==\u001b[39m u_idx) \u001b[38;5;241m|\u001b[39m (idx[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m==\u001b[39m u_idx)]\n\u001b[0;32m--> 461\u001b[0m     c \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcombinations\u001b[49m\u001b[43m(\u001b[49m\u001b[43msub_pos\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    462\u001b[0m     all_combinations\u001b[38;5;241m.\u001b[39mappend(c)\n\u001b[1;32m    463\u001b[0m all_pos \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mcat(all_combinations)\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current time: 2024-08-23 13:57:17.750107\n",
+      "\tLoss: 482.791 , R2: 0.063\n",
+      "\tTraining time (model/function): (0.818s / 0.408s)\n",
+      "\t\tper epoch (model/function): (0.058s/ 0.029s)\n",
+      "\t\tper 1k samples: 0.648s\n",
+      "('etas corr with lib_sizes (before refinement)', SignificanceResult(statistic=0.9855756636883769, pvalue=2.5036006799198427e-74))\n",
+      "\n",
+      "\n",
+      "final refinement step (after shift)...unfreezing all layers\n",
+      "optimizer:  Adam , criterion: PoissonLoss \n",
+      "epochs: 15 \n",
+      "early_stopping: 4\n",
+      "lr= 0.01, weight_decay= 0, dir weight= 0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:00<00:00, 35.68it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current time: 2024-08-23 13:57:18.214496\n",
+      "\tLoss: 245.504 , R2: 0.061\n",
+      "\tTraining time (model/function): (1.239s / 0.422s)\n",
+      "\t\tper epoch (model/function): (0.089s/ 0.030s)\n",
+      "\t\tper 1k samples: 0.669s\n",
+      "best loss 245.504\n",
+      "last five r2 values, by sequential filter optimization: ['0.061']\n",
+      "('etas corr with lib_sizes (before refinement)', SignificanceResult(statistic=0.9855756636883769, pvalue=2.5036006799198427e-74))\n",
+      "optimizing feature type dinuc\n",
+      "the optimization of dinucleotide features is skipped...\n",
+      "current kernel 140\n",
+      "\n",
+      "### next filter to optimize 140 \n",
+      "\n",
+      "FREEZING KERNELS\n",
+      "optimizing feature type mono\n",
+      "next kernels 140-209, n=70\n",
+      "filters mask None\n",
+      "optimizer:  Adam , criterion: PoissonLoss \n",
+      "epochs: 15 \n",
+      "early_stopping: 4\n",
+      "lr= 0.01, weight_decay= 0, dir weight= 0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:00<00:00, 37.17it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current time: 2024-08-23 13:57:18.696719\n",
+      "\tLoss: 463.912 , R2: 0.066\n",
+      "\tTraining time (model/function): (1.644s / 0.405s)\n",
+      "\t\tper epoch (model/function): (0.117s/ 0.029s)\n",
+      "\t\tper 1k samples: 0.643s\n",
+      "('etas corr with lib_sizes (before refinement)', SignificanceResult(statistic=0.9855756636883769, pvalue=2.5036006799198427e-74))\n",
+      "\n",
+      "\n",
+      "final refinement step (after shift)...unfreezing all layers\n",
+      "optimizer:  Adam , criterion: PoissonLoss \n",
+      "epochs: 15 \n",
+      "early_stopping: 4\n",
+      "lr= 0.01, weight_decay= 0, dir weight= 0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:00<00:00, 35.81it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current time: 2024-08-23 13:57:19.160106\n",
+      "\tLoss: 226.352 , R2: 0.067\n",
+      "\tTraining time (model/function): (2.065s / 0.420s)\n",
+      "\t\tper epoch (model/function): (0.147s/ 0.030s)\n",
+      "\t\tper 1k samples: 0.667s\n",
+      "best loss 226.352\n",
+      "last five r2 values, by sequential filter optimization: ['0.061', '0.067']\n",
+      "('etas corr with lib_sizes (before refinement)', SignificanceResult(statistic=0.9855756636883769, pvalue=2.5036006799198427e-74))\n",
+      "optimizing feature type dinuc\n",
+      "the optimization of dinucleotide features is skipped...\n",
+      "current kernel 210\n",
+      "\n",
+      "### next filter to optimize 210 \n",
+      "\n",
+      "FREEZING KERNELS\n",
+      "optimizing feature type mono\n",
+      "next kernels 210-279, n=70\n",
+      "filters mask None\n",
+      "optimizer:  Adam , criterion: PoissonLoss \n",
+      "epochs: 15 \n",
+      "early_stopping: 4\n",
+      "lr= 0.01, weight_decay= 0, dir weight= 0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:00<00:00, 36.95it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current time: 2024-08-23 13:57:19.644114\n",
+      "\tLoss: 445.218 , R2: 0.071\n",
+      "\tTraining time (model/function): (2.472s / 0.407s)\n",
+      "\t\tper epoch (model/function): (0.177s/ 0.029s)\n",
+      "\t\tper 1k samples: 0.647s\n",
+      "('etas corr with lib_sizes (before refinement)', SignificanceResult(statistic=0.9855756636883769, pvalue=2.5036006799198427e-74))\n",
+      "\n",
+      "\n",
+      "final refinement step (after shift)...unfreezing all layers\n",
+      "optimizer:  Adam , criterion: PoissonLoss \n",
+      "epochs: 15 \n",
+      "early_stopping: 4\n",
+      "lr= 0.01, weight_decay= 0, dir weight= 0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:00<00:00, 36.23it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current time: 2024-08-23 13:57:20.102829\n",
+      "\tLoss: 208.609 , R2: 0.072\n",
+      "\tTraining time (model/function): (2.887s / 0.415s)\n",
+      "\t\tper epoch (model/function): (0.206s/ 0.030s)\n",
+      "\t\tper 1k samples: 0.659s\n",
+      "best loss 208.609\n",
+      "last five r2 values, by sequential filter optimization: ['0.061', '0.067', '0.072']\n",
+      "('etas corr with lib_sizes (before refinement)', SignificanceResult(statistic=0.9855756636883769, pvalue=2.5036006799198427e-74))\n",
+      "optimizing feature type dinuc\n",
+      "the optimization of dinucleotide features is skipped...\n",
+      "current kernel 280\n",
+      "\n",
+      "### next filter to optimize 280 \n",
+      "\n",
+      "FREEZING KERNELS\n",
+      "optimizing feature type mono\n",
+      "next kernels 280-349, n=70\n",
+      "filters mask None\n",
+      "optimizer:  Adam , criterion: PoissonLoss \n",
+      "epochs: 15 \n",
+      "early_stopping: 4\n",
+      "lr= 0.01, weight_decay= 0, dir weight= 0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:00<00:00, 36.86it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current time: 2024-08-23 13:57:20.588174\n",
+      "\tLoss: 428.753 , R2: 0.076\n",
+      "\tTraining time (model/function): (3.296s / 0.409s)\n",
+      "\t\tper epoch (model/function): (0.235s/ 0.029s)\n",
+      "\t\tper 1k samples: 0.648s\n",
+      "('etas corr with lib_sizes (before refinement)', SignificanceResult(statistic=0.9855756636883769, pvalue=2.5036006799198427e-74))\n",
+      "\n",
+      "\n",
+      "final refinement step (after shift)...unfreezing all layers\n",
+      "optimizer:  Adam , criterion: PoissonLoss \n",
+      "epochs: 15 \n",
+      "early_stopping: 4\n",
+      "lr= 0.01, weight_decay= 0, dir weight= 0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:00<00:00, 36.44it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current time: 2024-08-23 13:57:21.044251\n",
+      "\tLoss: 193.622 , R2: 0.076\n",
+      "\tTraining time (model/function): (3.709s / 0.413s)\n",
+      "\t\tper epoch (model/function): (0.265s/ 0.029s)\n",
+      "\t\tper 1k samples: 0.655s\n",
+      "best loss 193.622\n",
+      "last five r2 values, by sequential filter optimization: ['0.061', '0.067', '0.072', '0.075']\n",
+      "('etas corr with lib_sizes (before refinement)', SignificanceResult(statistic=0.9855756636883769, pvalue=2.5036006799198427e-74))\n",
+      "optimizing feature type dinuc\n",
+      "the optimization of dinucleotide features is skipped...\n",
+      "\n",
+      "optimization finished:\n",
+      "total time: 3.708890438079834s\n",
+      "Time per epoch (total): 0.001 s\n"
      ]
     }
    ],
@@ -7258,7 +3731,7 @@
     "                                           prepare_knn=optimize_log_dynamic,\n",
     "                                           knn_free_weights=False,\n",
     "                                           adata=None if not optimize_log_dynamic else ad,\n",
-    "                                           dinuc_mode=None) # .cuda()\n",
+    "                                           dinuc_mode=None).cuda()\n",
     "\n",
     "    # initialize the reduce kernels\n",
     "    for i, mono_best in enumerate(pwms):\n",
@@ -7314,7 +3787,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [
     {
@@ -7323,15 +3796,15 @@
        "Text(0.5, 0, 'learned etas (per sample)')"
       ]
      },
-     "execution_count": 491,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 450x450 with 1 Axes>"
+       "<Figure size 500x500 with 1 Axes>"
       ]
      },
      "metadata": {},
diff --git a/docs/notebooks/single_cell/02_2_1_scrna_dentategyrus_train.ipynb b/docs/notebooks/single_cell/02_2_1_scrna_dentategyrus_train.ipynb
index 53a0b4b..2cc5c0f 100644
--- a/docs/notebooks/single_cell/02_2_1_scrna_dentategyrus_train.ipynb
+++ b/docs/notebooks/single_cell/02_2_1_scrna_dentategyrus_train.ipynb
@@ -5,7 +5,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Dentate gyrus (scRNA-seq) | Training with a RNA-dynamics kNN-graph"
+    "## Dentate gyrus (scRNA-seq) | Training with an RNA-dynamics kNN-graph"
    ]
   },
   {
diff --git a/docs/notebooks/single_cell/02_2_1_scrna_noack_priors_train.ipynb b/docs/notebooks/single_cell/02_2_1_scrna_noack_priors_train.ipynb
index b43d180..8f1d25c 100644
--- a/docs/notebooks/single_cell/02_2_1_scrna_noack_priors_train.ipynb
+++ b/docs/notebooks/single_cell/02_2_1_scrna_noack_priors_train.ipynb
@@ -5,7 +5,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Mouse neurogenesis scRNA-seq (Noack et al. 2022) | Training with a RNA-dynamics kNN-graph\n"
+    "## Mouse neurogenesis scRNA-seq (Noack et al. 2022) | Training with an RNA-dynamics kNN-graph\n"
    ]
   },
   {
diff --git a/mubind/datasets/__init__.py b/mubind/datasets/__init__.py
index 2c09cf7..067cc28 100644
--- a/mubind/datasets/__init__.py
+++ b/mubind/datasets/__init__.py
@@ -9,8 +9,9 @@
     simulate_data,
     simulate_xy,
     cisbp_hs, genre,
-    archetypes, archetypes_anno, archetypes_clu, # pwm datasets
+    archetypes, archetypes_anno, archetypes_clu, archetypes_pickle, # pwm datasets
     pancreas_multiome,
     pancreas_rna,
+    pancreas_rna_pytest,
     pancreas_atac,
 )
diff --git a/mubind/datasets/datasets.py b/mubind/datasets/datasets.py
index 852748a..6663a30 100644
--- a/mubind/datasets/datasets.py
+++ b/mubind/datasets/datasets.py
@@ -12,7 +12,8 @@
 import pandas as pd
 import os
 import pickle
-
+import urllib.request
+ 
 # Class for reading training/testing SELEX dataset files.
 class SelexDataset(tdata.Dataset):
     def __init__(self, df, n_rounds=None, enr_series=True, single_encoding_step=False, store_rev=False,
@@ -476,23 +477,46 @@ def genre(**kwargs):
     return pwms
 
 def archetypes_anno(**kwargs):
-    # read reference clusters
-    archetypes_dir = os.path.join(mb.bindome.constants.ANNOTATIONS_DIRECTORY, 'archetypes')
-    anno = pd.read_excel(os.path.join(archetypes_dir, 'motif_annotations.xlsx'), sheet_name='Archetype clusters')
+    url = kwargs['url']
+    # read reference clusters    
+    archetypes_dir = 'data/archetypes'
+    archetypes_path = os.path.join(archetypes_dir, 'motif_annotations.xlsx')
+
+    # save to avoid future redownloads
+    if not os.path.exists(archetypes_path):
+       if not os.path.exists(archetypes_dir):
+            os.makedirs(archetypes_dir)
+       urllib.request.urlretrieve(kwargs['url'], archetypes_path)
+
+    anno = pd.read_excel(archetypes_path, sheet_name='Archetype clusters')
     return anno
 
 def archetypes_clu(**kwargs):
-    archetypes_dir = os.path.join(mb.bindome.constants.ANNOTATIONS_DIRECTORY, 'archetypes')
-    clu = pd.read_excel(os.path.join(archetypes_dir, 'motif_annotations.xlsx'), sheet_name='Motifs')
+    url = kwargs['url']
+    archetypes_dir = 'data/archetypes'
+    archetypes_path = os.path.join(archetypes_dir, 'motif_annotations.xlsx')
+    clu = pd.read_excel(archetypes_path, sheet_name='Motifs')
     return clu
 
-def archetypes(**kwargs):
-    ppm_by_name = {}
-    archetypes_dir = os.path.join(mb.bindome.constants.ANNOTATIONS_DIRECTORY, 'archetypes')
+def archetypes_pickle(**kwargs):
+    # read reference clusters    
+    archetypes_dir = 'data/archetypes'
+    archetypes_path = os.path.join(archetypes_dir, 'archetypes_data.pkl')
 
-    anno = archetypes_anno(**kwargs)
-    clu = archetypes_anno(**kwargs)
+    # save to avoid future redownloads
+    if not os.path.exists(archetypes_path):
+       if not os.path.exists(archetypes_dir):
+            os.makedirs(archetypes_dir)
+       # print('downloading...')
+       urllib.request.urlretrieve(kwargs['url'], archetypes_path)
+
+    ppm_by_name = pickle.load(open(archetypes_path, 'rb'))
+    return ppm_by_name
 
+
+def archetypes_meme(**kwargs):
+    ppm_by_name = {}
+    archetypes_dir = 'data/archetypes'
     # read PFM across meme files
     for f in os.listdir(archetypes_dir):
         if f.endswith('.meme'):
@@ -513,7 +537,22 @@ def archetypes(**kwargs):
                     ppm.index = 'A', 'C', 'G', 'T'
                     ppm_by_name[name] = ppm
     print('# motifs loaded %i' % (len(ppm_by_name)))
+    return ppm_by_name
+
+def archetypes(**kwargs):
+    # annotation table
+    url = 'https://www.dropbox.com/scl/fi/odxcg72nj3djbfz6r9nq8/motif_annotations.xlsx?rlkey=qlbyx9m7dj6qqui9ct80q9ejc&dl=1'
+    kwargs['url'] = url
+    archetypes_dir = 'data/archetypes'
+    anno = archetypes_anno(**kwargs)
+    clu = archetypes_anno(**kwargs)
 
+    # PWM weights
+    url = 'https://www.dropbox.com/scl/fi/gytniua2uay1p6st0svh9/archetypes_data.pkl?rlkey=qe7mzhwaiqfpkjbdj31ijx193&dl=1'
+    kwargs['url'] = url
+    ppm_by_name = archetypes_pickle(**kwargs)
+
+    # print(clu)
     # return non-redundant groups
     reduced_groups = []
     for k in anno['Seed_motif']:
@@ -530,10 +569,27 @@ def pancreas_rna(
 ):
     from scanpy import read
     # rna
-    url = 'https://www.dropbox.com/scl/fi/ryb3q25n0kc2vw297f2xd/pancreas_multiome_2022_processed_rna_velocities_2024.h5ad?rlkey=in0qlpv038cn6wxrops1wsxgm&dl=0'
+    url = 'https://www.dropbox.com/scl/fi/ryb3q25n0kc2vw297f2xd/pancreas_multiome_2022_processed_rna_velocities_2024.h5ad?rlkey=in0qlpv038cn6wxrops1wsxgm&dl=1'
+    print(os.path.exists(file_path), file_path)
+    # print('reading RNA')
+    adata = read(file_path, backup_url=url, sparse=True, cache=True)
+    adata.var_names_make_unique()
+    # print('opening RNA successful')
+    return adata
+
+def pancreas_rna_pytest(
+    file_path: Optional[
+        Union[str, Path]
+    ] = "data/scatac/pancreas_multiome/pancreas_multiome_2022_processed_rna_velocities_2024_pytest.h5ad"
+):
+    from scanpy import read
+    # rna
+    url = 'https://www.dropbox.com/scl/fi/93hw0wru56ljryo6m17d9/pancreas_multiome_2022_processed_rna_velocities_2024_pytest.h5ad?rlkey=x8r14un3gu8ahyipcylwxytns&dl=1'
     print(os.path.exists(file_path), file_path)
+    # print('reading RNA')
     adata = read(file_path, backup_url=url, sparse=True, cache=True)
     adata.var_names_make_unique()
+    # print('opening RNA successful')
     return adata
 
 def pancreas_atac(
@@ -543,9 +599,11 @@ def pancreas_atac(
 ):
     from scanpy import read
     # atac
-    url = 'https://www.dropbox.com/scl/fi/53wv4v7tbnsmr12fbmea7/pancreas_multiome_2022_processed_atac.h5ad?rlkey=1kf352wya0pzffkn990wkbwmd&e=1&st=m6gv9hp5&dl=0'
+    url = 'https://www.dropbox.com/scl/fi/53wv4v7tbnsmr12fbmea7/pancreas_multiome_2022_processed_atac.h5ad?rlkey=1kf352wya0pzffkn990wkbwmd&e=1&st=m6gv9hp5&dl=1'
     print(os.path.exists(file_path), file_path)
+    print('reading ATAC')
     adata = read(file_path, backup_url=url, sparse=True, cache=True)
+    print('opening ATAC successful')
     adata.var_names_make_unique()
     return adata
 
diff --git a/mubind/models/models.py b/mubind/models/models.py
index 5d39b1a..004803d 100644
--- a/mubind/models/models.py
+++ b/mubind/models/models.py
@@ -828,10 +828,10 @@ def closure():
         self.r2_history += r2_history
 
     def corr_etas_libsizes(self, train):
-        etas = self.get_log_etas().detach().numpy().cpu().flatten() if self.device != 'cpu' else self.get_log_etas().detach().flatten()
-        lib_sizes = train.dataset.rounds.sum(axis=0).detach().numpy().cpu().flatten() if self.device != 'cpu' else train.dataset.rounds.sum(axis=0).flatten()
-        print('etas', etas, etas.shape, etas.device)
-        print('libsizes', lib_sizes, lib_sizes.shape)
+        etas = self.get_log_etas().detach().cpu().numpy().flatten() if self.device != 'cpu' else self.get_log_etas().detach().flatten()
+        lib_sizes = train.dataset.rounds.sum(axis=0) if self.device != 'cpu' else train.dataset.rounds.sum(axis=0).flatten()
+        # print('etas', etas, etas.shape, etas.device)
+        # print('libsizes', lib_sizes, lib_sizes.shape)
         return 'etas corr with lib_sizes (before refinement)', spearmanr(etas, lib_sizes)
 
     def optimize_iterative(self,
diff --git a/pyproject.toml b/pyproject.toml
index 5c9158d..6cacf95 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -20,12 +20,12 @@ maintainers = [
 urls.Documentation = "https://mubind.readthedocs.io/"
 urls.Source = "https://github.com/theislab/mubind"
 urls.Home-page = "https://github.com/theislab/mubind"
-version = "0.2.1"
+version = "0.2.2"
 requires-python = ">=3.9" # for GPU-rapids
 license = {file = "LICENSE"}
 readme = "README.md"
 dependencies = ["seaborn", "scikit-learn", "pandas", "unidecode", "matplotlib", "scipy", "numpy>=1.22", "torch",
-                "logomaker", "biopython", "numba", "pytest", "pytest-cov", "openpyxl", "tqdm", "anndata"]
+                "logomaker", "biopython", "numba", "pytest", "pytest-cov", "openpyxl", "tqdm", "scanpy"]
 
 [project.optional-dependencies]
 dev = [
diff --git a/tests/test_datasets.py b/tests/test_datasets.py
index dad8423..055af27 100644
--- a/tests/test_datasets.py
+++ b/tests/test_datasets.py
@@ -4,6 +4,7 @@
 import torch.optim as topti
 import torch.utils.data as tdata
 import mubind as mb
+import pytest
 
 def test_dataset_index_int():
     import warnings
@@ -49,7 +50,17 @@ def test_seq_conversion():
 
     assert (x2 == strs).all()
 
+def test_download_and_load_dataset():
+    import warnings
+    ad = mb.datasets.pancreas_rna_pytest()
+    return None
 
+@pytest.mark.filterwarnings("ignore::pytest.PytestUnraisableExceptionWarning")
+def test_archetypes():
+    import warnings
+    data = mb.datasets.archetypes()
+    return None
+    
 def test_dataset_memory_increase():
     import warnings
     warnings.filterwarnings("ignore", category=DeprecationWarning)