class_docstr: A Context that captures RunMetadata and performs profiling.
  
  ```python
    # Trace steps 100~200, profile at [150, 200] and dump profile at 200.
    with profile_context.ProfileContext('/tmp/train_dir',
                                        trace_steps=range(100, 200, 3),
                                        dump_steps=[200]) as pctx:
      opts = tf.profiler.ProfileOptionBuilder.time_and_memory()
      pctx.add_auto_profiling('op', opts, [150, 200])
      train_loop().
  
    # Tracing only.
    with profile_context.tfprof.ProfileContext('/tmp/train_dir') as pctx:
      # Run train/eval loop for at least few hundred steps. Profiles will be
      # dumped to train_dir. Use web UI or command line to do profiling.
      train_loop().
  
    # When session object is available, do explicit trace, profile and dump.
    with profile_context.ProfileContext('/tmp/train_dir',
                                        trace_steps=[],
                                        dump_steps=[]) as pctx:
      opts = tf.profiler.ProfileOptionBuilder.time_and_memory()
      pctx.trace_next_step()
      _ = session.run(train_op)
      pctx.profiler.profile_operations(options=opts)
  ```
  
  Args:
    profile_dir: Directory to store profiles.
    trace_steps: A list of session run steps to trace. If None, use
        pre-defined steps.
    dump_steps: A list of steps to dump the profile to `profile_dir`. If None,
        use pre-defined steps.
    enabled: If false, everything is disabled with minimal overhead. It allows
        user to only enable profiling when needed.
    debug: If true, also dumps the raw trace RunMetadata text file to
        profile_dir. And print debugging message. Useful for bug report.
docstr: Traces and profiles at some session run steps.
  
  Args:
    cmd: The profiling commands. (i.e. scope, op, python, graph)
    options: The profiling options.
    profile_steps: A list/set of integers. The profiling command and options
        will be run automatically at these integer steps. Each step is
        a session.run.
docstr_tok: ["Traces","and","profiles","at","some","session","run","steps",".","Args",":","cmd",":","The","profiling","commands",".","(","i.e",".","scope",",","op",",","python",",","graph",")","options",":","The","profiling","options",".","profile_steps",":","A","list\/set","of","integers",".","The","profiling","command","and","options","will","be","run","automatically","at","these","integer","steps",".","Each","step","is","a","session.run","."]
code: def add_auto_profiling(self, cmd, options, profile_steps):
      """Traces and profiles at some session run steps.
  
      Args:
        cmd: The profiling commands. (i.e. scope, op, python, graph)
        options: The profiling options.
        profile_steps: A list/set of integers. The profiling command and options
            will be run automatically at these integer steps. Each step is
            a session.run.
      """
      if not self._enabled:
          return
      self._auto_profiles.append((cmd, options, profile_steps[:]))
      self._slow_path_steps |= set(profile_steps)
      self._trace_steps |= set(profile_steps)
  
imports: import contextlib
  import os
  import random
  import sys
  import threading
  from tensorflow.core.protobuf import config_pb2
  from tensorflow.python.client import session
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import ops
  from tensorflow.python.platform import gfile
  from tensorflow.python.profiler import model_analyzer
  from tensorflow.python.util import _pywrap_tfprof
  from tensorflow.python.util import compat
label_desc: Use image node_id 3 for calling the ProfileContext obj's underlying member method code with example usage: obj.add_auto_profiling(cmd, options, profile_steps) without return types