address comments

Author: wanchaol

beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py

@@ -18,7 +18,7 @@
 # regarding data preprocessing, model theory and definition, and model
 # training.
 #
-# What is the TorchScript?
+# What is TorchScript?
 # ----------------------------
 #
 # During the research and development phase of a deep learning-based
@@ -53,12 +53,12 @@
 # will be recorded. In other words, the control flow itself is not
 # captured. To convert modules and functions containing data-dependent
 # control flow, a **scripting** mechanism is provided. The
-# ``torch.jit.script`` function takes module or function and does not
-# requires example inputs. Scripting then explicitly converts the module
-# or function code to TorchScript, including all possible control flow
-# routes. The one caveat with using scripting is that it only supports
-# a subset of Python, so you might need to rewrite the code to make it
-# compatible with TorchScript syntax.
+# ``torch.jit.script`` function/decorator takes a module or function and
+# does not requires example inputs. Scripting then explicitly converts
+# the module or function code to TorchScript, including all control flows.
+# One caveat with using scripting is that it only supports a subset of
+# Python, so you might need to rewrite the code to make it compatible
+# with the TorchScript syntax.
 #
 # For all details relating to the supported features, see the TorchScript
 #  `language reference <https://pytorch.org/docs/master/jit.html>`__. To
@@ -385,7 +385,7 @@ def forward(self, hidden, encoder_outputs):
 # TorchScript Notes:
 # ~~~~~~~~~~~~~~~~~~~~~~
 #
-# Similarly to the ``EncoderRNN```, this module does not contain any
+# Similarly to the ``EncoderRNN``, this module does not contain any
 # data-dependent control flow. Therefore, we can once again use
 # **tracing** to convert this model to TorchScript after it
 # is initialized and its parameters are loaded.
@@ -692,7 +692,7 @@ def evaluateExample(sentence, searcher, voc):
 # for some part of your models, you must call .to(device) to set the device
 # options of the models and .eval() to set the dropout layers to test mode
 # **before** tracing the models. `TracedModule` objects do not inherit the
-# ``to``` or ``eval``` methods. Since in this tutorial we are only using
+# ``to`` or ``eval`` methods. Since in this tutorial we are only using
 # scripting instead of tracing, we only need to do this before we do
 # evaluation (which is the same as we normally do in eager mode).
 #