Merge pull request #3895 from deven-gqc/master

fix callout renders

nbs/24_tutorial.image_sequence.ipynb

@@ -207,6 +207,7 @@
    "metadata": {},
    "source": [
     "inside, you will find  one video per instance, the videos are in `.avi` format. We will need to convert each video to a sequence of images to able to work with our fastai vision toolset.\n",
+    "\n",
     ":::{.callout-note}\n",
     "\n",
     "torchvision has a built-in video reader that may be capable of simplifying this task\n",
@@ -1160,6 +1161,7 @@
    "metadata": {},
    "source": [
     "We will make use of the `MixedPrecision` callback to speed up our training (by calling `to_fp16` on the learner object).\n",
+    "\n",
     ":::{.callout-note}\n",
     "\n",
     "The `TimeDistributed` layer is memory hungry (it pivots the image sequence to the batch dimesion) so if you get OOM errors, try reducing the batchsize.\n",

nbs/40_tabular.core.ipynb

@@ -2561,6 +2561,7 @@
    "metadata": {},
    "source": [
     "We can make new test datasets based on the training data with the `to.new()`\n",
+    "\n",
     ":::{.callout-note}\n",
     "\n",
     "Since machine learning models can't magically understand categories it was never trained on, the data should reflect this. If there are different missing values in your test data you should address this before training\n",

nbs/examples/migrating_pytorch_verbose.ipynb

@@ -473,6 +473,7 @@
     "We will walk through a quick example below.\n",
     "\n",
     "First let's save the model weights:\n",
+    "\n",
     ":::{.callout-note}\n",
     "\n",
     "Generally when doing this approach you should also store the source code to build the model as well\n",