Commented suggested example variables

beginner_source/PyTorch Cheat.md

@@ -133,14 +133,14 @@ See [nn](https://pytorch.org/docs/stable/nn.html)
 ### Loss Functions
 
 ```
-nn.X where for example X is ...       # BCELoss, CrossEntropyLoss, L1Loss, MSELoss, NLLLoss, SoftMarginLoss, MultiLabelSoftMarginLoss, CosineEmbeddingLoss, KLDivLoss, MarginRankingLoss, HingeEmbeddingLoss or CosineEmbeddingLoss
+nn.X                                        # where X is BCELoss, CrossEntropyLoss, L1Loss, MSELoss, NLLLoss, SoftMarginLoss, MultiLabelSoftMarginLoss, CosineEmbeddingLoss, KLDivLoss, MarginRankingLoss, HingeEmbeddingLoss or CosineEmbeddingLoss
 ```
 See [loss functions](https://pytorch.org/docs/stable/nn.html#loss-functions)
 
 ### Activation Functions
 
 ```
-nn.X where for example X is ...       # ReLU, ReLU6, ELU, SELU, PReLU, LeakyReLU, Threshold, HardTanh, Sigmoid, Tanh, LogSigmoid, Softplus, SoftShrink, Softsign, TanhShrink, Softmin, Softmax, Softmax2d or LogSoftmax
+nn.X                                  # where X is ReLU, ReLU6, ELU, SELU, PReLU, LeakyReLU, Threshold, HardTanh, Sigmoid, Tanh, LogSigmoid, Softplus, SoftShrink, Softsign, TanhShrink, Softmin, Softmax, Softmax2d or LogSoftmax
 ```
 See [activation functions](https://pytorch.org/docs/stable/nn.html#non-linear-activations-weighted-sum-nonlinearity)
 
@@ -149,7 +149,7 @@ See [activation functions](https://pytorch.org/docs/stable/nn.html#non-linear-ac
 ```
 opt = optim.x(model.parameters(), ...)      # create optimizer
 opt.step()                                  # update weights
-optim.X where for example X is ...          # SGD, Adadelta, Adagrad, Adam, SparseAdam, Adamax, ASGD, LBFGS, RMSProp or Rprop
+optim.X                                     # where X is SGD, Adadelta, Adagrad, Adam, SparseAdam, Adamax, ASGD, LBFGS, RMSProp or Rprop
 ```
 See [optimizers](https://pytorch.org/docs/stable/optim.html)
 
@@ -158,7 +158,7 @@ See [optimizers](https://pytorch.org/docs/stable/optim.html)
 ```
 scheduler = optim.X(optimizer,...)      # create lr scheduler
 scheduler.step()                        # update lr at start of epoch
-optim.lr_scheduler.X where ...          # LambdaLR, StepLR, MultiStepLR, ExponentialLR or ReduceLROnPLateau
+optim.lr_scheduler.X                    # where X is LambdaLR, StepLR, MultiStepLR, ExponentialLR or ReduceLROnPLateau
 ```
 See [learning rate scheduler](https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate)
 
@@ -179,7 +179,7 @@ See [datasets](https://pytorch.org/docs/stable/data.html?highlight=dataset#torch
 ```
 DataLoader(dataset, batch_size=1, ...)      # loads data batches agnostic of structure of individual data points
 sampler.Sampler(dataset,...)                # abstract class dealing with ways to sample from dataset
-sampler.XSampler where ...                  # Sequential, Random, Subset, WeightedRandom or Distributed
+sampler.XSampler                            # where X is Sequential, Random, Subset, WeightedRandom or Distributed
 ```
 See [dataloader](https://pytorch.org/docs/stable/data.html?highlight=dataloader#torch.utils.data.DataLoader)
 

beginner_source/ptcheat.rst

@@ -189,7 +189,7 @@ Loss Functions
 
 .. code-block:: python
 
-    nn.X where for example X is ...       # BCELoss, CrossEntropyLoss, 
+    nn.X                                  # where X is BCELoss, CrossEntropyLoss, 
                                           # L1Loss, MSELoss, NLLLoss, SoftMarginLoss,
                                           # MultiLabelSoftMarginLoss, CosineEmbeddingLoss, 
                                           # KLDivLoss, MarginRankingLoss, HingeEmbeddingLoss 
@@ -203,7 +203,7 @@ Activation Functions
 
 .. code-block:: python
 
-    nn.X where for example X is ...       # ReLU, ReLU6, ELU, SELU, PReLU, LeakyReLU, 
+    nn.X                                  # where X is ReLU, ReLU6, ELU, SELU, PReLU, LeakyReLU, 
                                           # Threshold, HardTanh, Sigmoid, Tanh, 
                                           # LogSigmoid, Softplus, SoftShrink, 
                                           # Softsign, TanhShrink, Softmin, Softmax, 
@@ -219,7 +219,7 @@ Optimizers
 
     opt = optim.x(model.parameters(), ...)      # create optimizer
     opt.step()                                  # update weights
-    optim.X where for example X is ...          # SGD, Adadelta, Adagrad, Adam, 
+    optim.X                                     # where X is SGD, Adadelta, Adagrad, Adam, 
                                                 # SparseAdam, Adamax, ASGD, 
                                                 # LBFGS, RMSProp or Rprop
 
@@ -232,8 +232,8 @@ Learning rate scheduling
 
     scheduler = optim.X(optimizer,...)      # create lr scheduler
     scheduler.step()                        # update lr at start of epoch
-    optim.lr_scheduler.X where ...          # LambdaLR, StepLR, MultiStepLR, 
-    					    # ExponentialLR or ReduceLROnPLateau
+    optim.lr_scheduler.X                    # where X is LambdaLR, StepLR, MultiStepLR, 
+                  # ExponentialLR or ReduceLROnPLateau
 
 See `learning rate
 scheduler <https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate>`__