message: Add more detail to the tf.debugging.set_log_device_placement docstring.
diff: @@ -2126,7 +2126,32 @@ def get_log_device_placement():
  @tf_export("debugging.set_log_device_placement")
  def set_log_device_placement(enabled):
  - """Set if device placements should be logged.
  + """Turns logging for device placement decisions on or off.
  +
  + Operations execute on a particular device, producing and consuming tensors on
  + that device. This may change the performance of the operation or require
  + TensorFlow to copy data to or from an accelerator, so knowing where operations
  + execute is useful for debugging performance issues.
  +
  + For more advanced profiling, use the [TensorFlow
  + profiler](https://www.tensorflow.org/guide/profiler).
  +
  + Device placement for operations is typically controlled by a `tf.device`
  + scope, but there are exceptions, for example operations on a `tf.Variable`
  + which follow the initial placement of the variable. Turning off soft device
  + placement (with `tf.config.set_soft_device_placement`) provides more explicit
  + control.
  +
  + >>> tf.debugging.set_log_device_placement(True)
  + >>> tf.ones([])
  + >>> # [...] op Fill in device /job:localhost/replica:0/task:0/device:GPU:0
  + >>> with tf.device("CPU"):
  + ... tf.ones([])
  + >>> # [...] op Fill in device /job:localhost/replica:0/task:0/device:CPU:0
  + >>> tf.debugging.set_log_device_placement(False)
  +
  + Turning on `tf.debugging.set_log_device_placement` also logs the placement of
  + ops inside `tf.function` when the function is called.
  Args:
  enabled: Whether to enabled device placement logging.
  