Move Inspector Tutorial Code into individual code cells (#814)

Summary:

Run linter on the code portions and separate them into different notebook cells

---
Note: The generated Cell types are being set to markdown and that is intentional. Normally code cells would be best, but due to usage of runtime and python sections in this notebook, code cells would result in the documentation triggering a suite of build errors

Differential Revision: D50147708

Author: Jack-Khuu

docs/source/tutorials_source/sdk-integration-tutorial.py

@@ -61,6 +61,7 @@
 #
 #   # Imports
 #   import copy
+#
 #   import torch
 #
 #   from executorch.examples.models.mobilenet_v2 import MV2Model
@@ -81,7 +82,9 @@
 #       model.get_example_inputs(),
 #   )
 #
-#   edge_program_manager: EdgeProgramManager = to_edge(aten_model, compile_config=EdgeCompileConfig(_check_ir_validity=True))
+#   edge_program_manager: EdgeProgramManager = to_edge(
+#       aten_model, compile_config=EdgeCompileConfig(_check_ir_validity=True)
+#   )
 #   edge_program_manager_copy = copy.deepcopy(edge_program_manager)
 #   et_program_manager: ExecutorchProgramManager = edge_program_manager_copy.to_executorch()
 #
@@ -121,7 +124,7 @@
 # Note: An ``ETRecord`` is not required. If an ``ETRecord`` is not provided,
 # the Inspector will show runtime results without operator correlation.
 #
-# To visualize all runtime events, call ``print_data_tabular``::
+# To visualize all runtime events, call Inspector's ``print_data_tabular``::
 #
 #   from executorch.sdk import Inspector
 #
@@ -130,12 +133,11 @@
 #   inspector.print_data_tabular()
 #
 
-
 ######################################################################
 # Analyzing with an Inspector
 # ---------------------------
 #
-# ``Inspector`` provides 2 ways of accessing ingested information: `EventBlocks <../sdk-inspector.rst>`__
+# ``Inspector`` provides 2 ways of accessing ingested information: `EventBlocks <../sdk-inspector.html>`__
 # and ``DataFrames``. These mediums give users the ability to perform custom
 # analysis about their model performance.
 #
@@ -144,26 +146,30 @@
 #   # Set Up
 #
 #   import pprint as pp
-#   import pandas as pd
 #
-#   pd.set_option('display.max_colwidth', None)
-#   pd.set_option('display.max_columns', None)
+#   import pandas as pd
 #
+#   pd.set_option("display.max_colwidth", None)
+#   pd.set_option("display.max_columns", None)
+
+######################################################################
 # If a user wants the raw profiling results, they would do something similar to
 # finding the raw runtime data of an ``addmm.out`` event::
 #
 #   for event_block in inspector.event_blocks:
 #       # Via EventBlocks
 #       for event in event_block.events:
-#           if event.name == 'native_call_addmm.out':
+#           if event.name == "native_call_addmm.out":
 #               print(event.name, event.perf_data.raw)
 #
 #       # Via Dataframe
 #       df = event_block.to_dataframe()
-#       df = df[df.event_name == 'native_call_addmm.out']
-#       print(df[['event_name', 'raw']])
+#       df = df[df.event_name == "native_call_addmm.out"]
+#       print(df[["event_name', 'raw"]])
 #       print()
 #
+
+######################################################################
 # If a user wants to trace an operator back to their model code, they would do
 # something similar to finding the module hierarchy and stack trace of the
 # slowest ``convolution.out`` call::
@@ -172,35 +178,38 @@
 #       # Via EventBlocks
 #       slowest = None
 #       for event in event_block.events:
-#           if event.name == 'native_call_convolution.out':
+#           if event.name == "native_call_convolution.out":
 #               if slowest is None or event.perf_data.p50 > slowest.perf_data.p50:
 #                   slowest = event
 #       if slowest is not None:
 #           print(slowest.name)
 #           print()
 #           pp.pprint(slowest.stack_traces)
 #           print()
-#           pp.pprint(slowest.module_hierarchy
+#           pp.pprint(slowest.module_hierarchy)
 #
 #       # Via Dataframe
 #       df = event_block.to_dataframe()
-#       df = df[df.event_name == 'native_call_convolution.out']
+#       df = df[df.event_name == "native_call_convolution.out"]
 #       if len(df) > 0:
-#           slowest = df.loc[df['p50'].idxmax()]
+#           slowest = df.loc[df["p50"].idxmax()]
 #           print(slowest.event_name)
 #           print()
 #           pp.pprint(slowest.stack_traces)
 #           print()
 #           pp.pprint(slowest.module_hierarchy)
 #
-# If a user wants the total runtime of a module::
+
+######################################################################
+# If a user wants the total runtime of a module, they can use
+# ``find_total_for_module``::
 #
 #   print(inspector.find_total_for_module("L__self___features"))
 #   print(inspector.find_total_for_module("L__self___features_14"))
-#
+
+######################################################################
 # Note: ``find_total_for_module`` is a special first class method of
 # `Inspector <../sdk-inspector.html>`__
-#
 
 ######################################################################
 # Conclusion