fix(optimization_tutorial): bullet list indentation in colab

beginner_source/basics/optimization_tutorial.py

@@ -76,9 +76,10 @@ def forward(self, x):
 # (`read more <https://pytorch.org/tutorials/beginner/hyperparameter_tuning_tutorial.html>`__ about hyperparameter tuning)
 #
 # We define the following hyperparameters for training:
-#  - **Number of Epochs** - the number of times to iterate over the dataset
-#  - **Batch Size** - the number of data samples propagated through the network before the parameters are updated
-#  - **Learning Rate** - how much to update models parameters at each batch/epoch. Smaller values yield slow learning speed, while large values may result in unpredictable behavior during training.
+#
+# - **Number of Epochs** - the number of times to iterate over the dataset
+# - **Batch Size** - the number of data samples propagated through the network before the parameters are updated
+# - **Learning Rate** - how much to update models parameters at each batch/epoch. Smaller values yield slow learning speed, while large values may result in unpredictable behavior during training.
 #
 
 learning_rate = 1e-3
@@ -95,8 +96,9 @@ def forward(self, x):
 # iteration of the optimization loop is called an **epoch**.
 #
 # Each epoch consists of two main parts:
-#  - **The Train Loop** - iterate over the training dataset and try to converge to optimal parameters.
-#  - **The Validation/Test Loop** - iterate over the test dataset to check if model performance is improving.
+#
+# - **The Train Loop** - iterate over the training dataset and try to converge to optimal parameters.
+# - **The Validation/Test Loop** - iterate over the test dataset to check if model performance is improving.
 #
 # Let's briefly familiarize ourselves with some of the concepts used in the training loop. Jump ahead to
 # see the :ref:`full-impl-label` of the optimization loop.
@@ -134,9 +136,10 @@ def forward(self, x):
 
 #####################################
 # Inside the training loop, optimization happens in three steps:
-#  * Call ``optimizer.zero_grad()`` to reset the gradients of model parameters. Gradients by default add up; to prevent double-counting, we explicitly zero them at each iteration.
-#  * Backpropagate the prediction loss with a call to ``loss.backward()``. PyTorch deposits the gradients of the loss w.r.t. each parameter.
-#  * Once we have our gradients, we call ``optimizer.step()`` to adjust the parameters by the gradients collected in the backward pass.
+#
+# - Call ``optimizer.zero_grad()`` to reset the gradients of model parameters. Gradients by default add up; to prevent double-counting, we explicitly zero them at each iteration.
+# - Backpropagate the prediction loss with a call to ``loss.backward()``. PyTorch deposits the gradients of the loss w.r.t. each parameter.
+# - Once we have our gradients, we call ``optimizer.step()`` to adjust the parameters by the gradients collected in the backward pass.
 
 
 ########################################