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: Maybe dump the profile file.
docstr_tok: ["Maybe","dump","the","profile","file","."]
code: def _maybe_dump(self, step):
      """Maybe dump the profile file."""
      if not (step in self._dump_steps or self._dump_next_step):
          return
      if self._debug:
          sys.stderr.write("debug: dumping file at step: %d\n" % step)
      gfile.MakeDirs(self._profiler_dir)
  
      filename = os.path.join(
          compat.as_bytes(self._profiler_dir),
          compat.as_bytes("profile_%d" % step),
      )
      self.profiler._write_profile(filename)
  
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 9 for calling the ProfileContext obj's underlying member method code with example usage: obj._maybe_dump(step) without return types