class_docstr: Context manager that generates a trace event in the profiler.
  
  A trace event will start when entering the context, and stop and save the
  result to the profiler when exiting the context. Open TensorBoard Profile tab
  and choose trace viewer to view the trace event in the timeline.
  
  Trace events are created only when the profiler is enabled. More information
  on how to use the profiler can be found at
  https://tensorflow.org/guide/profiler
  
  Example usage:
  ```python
  tf.profiler.experimental.start('logdir')
  for step in range(num_steps):
    # Creates a trace event for each training step with the step number.
    with tf.profiler.experimental.Trace("Train", step_num=step, _r=1):
      train_fn()
  tf.profiler.experimental.stop()
  ```
docstr: Creates a trace event in the profiler.
  
  Args:
    name: The name of the trace event.
    **kwargs: Keyword arguments added to the trace event.
              Both the key and value are of types that
              can be converted to strings, which will be
              interpreted by the profiler according to the
              traceme name.
  
    Example usage:
  
    ```python
  
      tf.profiler.experimental.start('logdir')
      for step in range(num_steps):
        # Creates a trace event for each training step with the
        # step number.
        with tf.profiler.experimental.Trace("Train", step_num=step):
          train_fn()
      tf.profiler.experimental.stop()
  
    ```
    The example above uses the keyword argument "step_num" to specify the
    training step being traced.
code: def __init__(self, name, **kwargs):
      """Creates a trace event in the profiler.
  
      Args:
        name: The name of the trace event.
        **kwargs: Keyword arguments added to the trace event.
                  Both the key and value are of types that
                  can be converted to strings, which will be
                  interpreted by the profiler according to the
                  traceme name.
  
        Example usage:
  
        ```python
  
          tf.profiler.experimental.start('logdir')
          for step in range(num_steps):
            # Creates a trace event for each training step with the
            # step number.
            with tf.profiler.experimental.Trace("Train", step_num=step):
              train_fn()
          tf.profiler.experimental.stop()
  
        ```
        The example above uses the keyword argument "step_num" to specify the
        training step being traced.
      """
      if enabled:
          # Creating _pywrap_traceme.TraceMe starts the clock.
          self._traceme = _pywrap_traceme.TraceMe(name, **kwargs)
      else:
          self._traceme = None
  
file_path: tensorflow-upstream/tensorflow/python/profiler/trace.py
imports: import functools
  from tensorflow.python.profiler.internal import _pywrap_traceme
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 1 to create a new Trace object from inherited base classes: object with example: obj = Trace(name)