class_docstr: Profiler context manager.
  
  Args:
      activities (iterable): list of activity groups (CPU, CUDA) to use in profiling, supported values:
          ``torch.profiler.ProfilerActivity.CPU``, ``torch.profiler.ProfilerActivity.CUDA``.
          Default value: ProfilerActivity.CPU and (when available) ProfilerActivity.CUDA.
      schedule (Callable): callable that takes step (int) as a single parameter and returns
          ``ProfilerAction`` value that specifies the profiler action to perform at each step.
      on_trace_ready (Callable): callable that is called at each step when ``schedule``
          returns ``ProfilerAction.RECORD_AND_SAVE`` during the profiling.
      record_shapes (bool): save information about operator's input shapes.
      profile_memory (bool): track tensor memory allocation/deallocation.
      with_stack (bool): record source information (file and line number) for the ops.
      with_flops (bool): use formula to estimate the FLOPs (floating point operations) of specific operators
          (matrix multiplication and 2D convolution).
      with_modules (bool): record module hierarchy (including function names)
          corresponding to the callstack of the op. e.g. If module A's forward call's
          module B's forward which contains an aten::add op,
          then aten::add's module hierarchy is A.B
          Note that this support exist, at the moment, only for TorchScript models
          and not eager mode models.
      experimental_config (_ExperimentalConfig) : A set of experimental options
          used for Kineto library features. Note, backward compatibility is not guaranteed.
  
      use_cuda (bool):
          .. deprecated:: 1.8.1
              use ``activities`` instead.
  
  .. note::
      Use :func:`~torch.profiler.schedule` to generate the callable schedule.
      Non-default schedules are useful when profiling long training jobs
      and allow the user to obtain multiple traces at the different iterations
      of the training process.
      The default schedule simply records all the events continuously for the
      duration of the context manager.
  
  .. note::
      Use :func:`~torch.profiler.tensorboard_trace_handler` to generate result files for TensorBoard:
  
      ``on_trace_ready=torch.profiler.tensorboard_trace_handler(dir_name)``
  
      After profiling, result files can be found in the specified directory. Use the command:
  
      ``tensorboard --logdir dir_name``
  
      to see the results in TensorBoard.
      For more information, see
      `PyTorch Profiler TensorBoard Plugin <https://github.com/pytorch/kineto/tree/master/tb_plugin>`__
  
  .. note::
      Enabling shape and stack tracing results in additional overhead.
      When record_shapes=True is specified, profiler will temporarily hold references to the tensors;
      that may further prevent certain optimizations that depend on the reference count and introduce
      extra tensor copies.
  
  Examples:
  
  .. code-block:: python
  
      with torch.profiler.profile(
          activities=[
              torch.profiler.ProfilerActivity.CPU,
              torch.profiler.ProfilerActivity.CUDA,
          ]
      ) as p:
          code_to_profile()
      print(p.key_averages().table(
          sort_by="self_cuda_time_total", row_limit=-1))
  
  Using the profiler's ``schedule``, ``on_trace_ready`` and ``step`` functions:
  
  .. code-block:: python
  
      # Non-default profiler schedule allows user to turn profiler on and off
      # on different iterations of the training loop;
      # trace_handler is called every time a new trace becomes available
      def trace_handler(prof):
          print(prof.key_averages().table(
              sort_by="self_cuda_time_total", row_limit=-1))
          # prof.export_chrome_trace("/tmp/test_trace_" + str(prof.step_num) + ".json")
  
      with torch.profiler.profile(
          activities=[
              torch.profiler.ProfilerActivity.CPU,
              torch.profiler.ProfilerActivity.CUDA,
          ],
  
          # In this example with wait=1, warmup=1, active=2, repeat=1,
          # profiler will skip the first step/iteration,
          # start warming up on the second, record
          # the third and the forth iterations,
          # after which the trace will become available
          # and on_trace_ready (when set) is called;
          # the cycle repeats starting with the next step
  
          schedule=torch.profiler.schedule(
              wait=1,
              warmup=1,
              active=2,
              repeat=1),
          on_trace_ready=trace_handler
          # on_trace_ready=torch.profiler.tensorboard_trace_handler('./log')
          # used when outputting for tensorboard
          ) as p:
              for iter in range(N):
                  code_iteration_to_profile(iter)
                  # send a signal to the profiler that the next iteration has started
                  p.step()
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code: def __exit__(self, exc_type, exc_val, exc_tb):
      self.stop()
      prof.KinetoStepTracker.erase_step_count(PROFILER_STEP_NAME)
  
imports: import gzip
  import json
  import os
  import tempfile
  from enum import Enum
  from functools import partial
  from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
  from warnings import warn
  import torch
  import torch.autograd.profiler
  from torch._C import _get_privateuse1_backend_name
  from torch._C._profiler import _add_execution_trace_observer, _disable_execution_trace_observer, _enable_execution_trace_observer, _ExperimentalConfig, _remove_execution_trace_observer
  from torch.autograd import kineto_available, ProfilerActivity
  from torch.profiler._memory_profiler import MemoryProfile, MemoryProfileTimeline
label_desc: Use image node_id 3 for calling the profile obj's underlying member method code with example usage: obj.__exit__(exc_type, exc_val, exc_tb) without return types