class_docstr: Options for finer control over the profiler.
  
  Use `tf.profiler.experimental.ProfilerOptions` to control `tf.profiler`
  behavior.
  
  Fields:
    host_tracer_level: Adjust CPU tracing level. Values are: `1` - critical info
      only, `2` - info, `3` - verbose. [default value is `2`]
    python_tracer_level: Toggle tracing of Python function calls. Values are:
      `1` - enabled, `0` - disabled [default value is `0`]
    device_tracer_level: Adjust device (TPU/GPU) tracing level. Values are:
      `1` - enabled, `0` - disabled [default value is `1`]
    delay_ms: Requests for all hosts to start profiling at a timestamp that is
      `delay_ms` away from the current time. `delay_ms` is in milliseconds. If
      zero, each host will start profiling immediately upon receiving the
      request. Default value is `None`, allowing the profiler guess the best
      value.
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code: def __new__(
      cls,
      host_tracer_level=2,
      python_tracer_level=0,
      device_tracer_level=1,
      delay_ms=None,
  ):
      return super(ProfilerOptions, cls).__new__(
          cls,
          host_tracer_level,
          python_tracer_level,
          device_tracer_level,
          delay_ms,
      )
  
file_path: tensorflow-upstream/tensorflow/python/profiler/profiler_v2.py
imports: import collections
  import threading
  from tensorflow.python.framework import errors
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.profiler.internal import _pywrap_profiler
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 1 for calling the ProfilerOptions obj's underlying member method code with example usage: obj.__new__(cls, host_tracer_level, python_tracer_level, device_tracer_level, delay_ms) and returns: super