docs(datasets) Fix Flower Datasets docs (#3929)

datasets/doc/source/conf.py

@@ -111,7 +111,7 @@ def find_test_modules(package_path):
 # Sphinx redirects, implemented after the doc filename changes.
 # To prevent 404 errors and redirect to the new pages.
 redirects = {
-    "how-to-visualize-label-distribution.html": "tutorial-visualize-label-distribution.html",
+    "how-to-visualize-label-distribution": "tutorial-visualize-label-distribution.html",
 }
 
 # -- Options for HTML output -------------------------------------------------

datasets/doc/source/tutorial-quickstart.ipynb

@@ -391,8 +391,11 @@
     "## Use with PyTorch/NumPy/TensorFlow\n",
     "\n",
     "For more detailed instructions, go to:\n",
+    "\n",
     "* [how-to-use-with-pytorch](https://flower.ai/docs/datasets/how-to-use-with-pytorch.html)\n",
+    "\n",
     "* [how-to-use-with-numpy](https://flower.ai/docs/datasets/how-to-use-with-numpy.html)\n",
+    "\n",
     "* [how-to-use-with-tensorflow](https://flower.ai/docs/datasets/how-to-use-with-tensorflow.html)"
    ]
   },
@@ -443,7 +446,9 @@
    "cell_type": "markdown",
    "id": "b93678a5",
    "metadata": {},
-   "source": "The `Dataloader` created this way does not return a `Tuple` when iterating over it but a `Dict` with the names of the columns as keys and features as values. Look below for an example."
+   "source": [
+    "The `Dataloader` created this way does not return a `Tuple` when iterating over it but a `Dict` with the names of the columns as keys and features as values. Look below for an example."
+   ]
   },
   {
    "cell_type": "code",

datasets/doc/source/tutorial-use-partitioners.ipynb

@@ -40,7 +40,7 @@
    "source": [
     "### `IidPartitioner` Creation\n",
     "\n",
-    "Let's create (instantiate) the most basic partitioner, [`IidPartitioner`](https://flower.ai/docs/datasets/ref-api/flwr_datasets.partitioner.IidPartitioner.html#flwr_datasets.partitioner.IidPartitioner) and learn how it interacts with `FederatedDataset`."
+    "Let's create (instantiate) the most basic partitioner, [IidPartitioner](https://flower.ai/docs/datasets/ref-api/flwr_datasets.partitioner.IidPartitioner.html#flwr_datasets.partitioner.IidPartitioner) and learn how it interacts with `FederatedDataset`."
    ]
   },
   {
@@ -165,7 +165,7 @@
    "source": [
     "#### Creating non-IID partitions: Use ``PathologicalPartitioner``\n",
     "\n",
-    "Now, we are going to create partitions that have only a subset of labels in each partition by using [`PathologicalPartitioner`](https://flower.ai/docs/datasets/ref-api/flwr_datasets.partitioner.PathologicalPartitioner.html#flwr_datasets.partitioner.PathologicalPartitioner). In this scenario we have the exact control about the number of unique labels on each partition. The smaller the number is the more heterogenous the division gets. Let's have a look at how it works with `num_classes_per_partition=2`."
+    "Now, we are going to create partitions that have only a subset of labels in each partition by using [PathologicalPartitioner](https://flower.ai/docs/datasets/ref-api/flwr_datasets.partitioner.PathologicalPartitioner.html#flwr_datasets.partitioner.PathologicalPartitioner). In this scenario we have the exact control about the number of unique labels on each partition. The smaller the number is the more heterogenous the division gets. Let's have a look at how it works with `num_classes_per_partition=2`."
    ]
   },
   {
@@ -261,7 +261,7 @@
    "source": [
     "#### Creating non-IID partitions: Use ``DirichletPartitioner``\n",
     "\n",
-    "With the [`DirichletParitioner`](https://flower.ai/docs/datasets/ref-api/flwr_datasets.partitioner.DirichletPartitioner.html#flwr_datasets.partitioner.DirichletPartitioner), the primary tool for controlling heterogeneity is the `alpha` parameter; the smaller the value gets, the more heterogeneous the federated datasets are. Instead of choosing the exact number of classes on each partition, here we sample the probability distribution from the Dirichlet distribution, which tells how the samples associated with each class will be divided."
+    "With the [DirichletPartitioner](https://flower.ai/docs/datasets/ref-api/flwr_datasets.partitioner.DirichletPartitioner.html#flwr_datasets.partitioner.DirichletPartitioner), the primary tool for controlling heterogeneity is the `alpha` parameter; the smaller the value gets, the more heterogeneous the federated datasets are. Instead of choosing the exact number of classes on each partition, here we sample the probability distribution from the Dirichlet distribution, which tells how the samples associated with each class will be divided."
    ]
   },
   {