diff --git a/generation/2d_vqgan/2d_vqgan_tutorial.ipynb b/generation/2d_vqgan/2d_vqgan_tutorial.ipynb
index 0b54d5fdd..156d547ec 100644
--- a/generation/2d_vqgan/2d_vqgan_tutorial.ipynb
+++ b/generation/2d_vqgan/2d_vqgan_tutorial.ipynb
@@ -624,7 +624,7 @@
     "val_samples = np.linspace(val_interval, max_epochs, int(max_epochs / val_interval))\n",
     "num_inters = min(len(intermediary_images), 4)\n",
     "fig, ax = plt.subplots(nrows=num_inters, ncols=1, sharey=True)\n",
-    "ax=ensure_tuple(ax)\n",
+    "ax = ensure_tuple(ax)\n",
     "\n",
     "ax = ensure_tuple(ax)\n",
     "for image_n in range(num_inters):\n",
diff --git a/generation/2d_vqvae/2d_vqvae_tutorial.ipynb b/generation/2d_vqvae/2d_vqvae_tutorial.ipynb
index 66c32e697..100a490ac 100644
--- a/generation/2d_vqvae/2d_vqvae_tutorial.ipynb
+++ b/generation/2d_vqvae/2d_vqvae_tutorial.ipynb
@@ -542,7 +542,7 @@
     "# Plot every evaluation as a new line and example as columns\n",
     "val_samples = np.linspace(val_interval, max_epochs, int(max_epochs / val_interval))\n",
     "fig, ax = plt.subplots(nrows=len(val_samples), ncols=1, sharey=True)\n",
-    "ax=ensure_tuple(ax)\n",
+    "ax = ensure_tuple(ax)\n",
     "fig.set_size_inches(18.5, 30.5)\n",
     "for image_n in range(len(val_samples)):\n",
     "    reconstructions = torch.reshape(intermediary_images[image_n], (64 * n_example_images, 64)).T\n",