message: Keras extension: reword description of set_asynchronous_callbacks()
  Summary: TF2.5 only
  Reviewers: #tensorflow, #framework_ip_review_-_any_oss_or_third-party_code_use_has_been_approved, markf
  Maniphest Tasks: T45279
diff: @@ -28,7 +28,7 @@ from tensorflow.python.util import deprecation
  class SequentialLayerPipelineStageAssignment:
  - """A class used to indicate in which pipeline stage a layer in a `Sequential`
  + """A class used to indicate which pipeline stage a layer in a `Sequential`
  model should be executed in.
  """
  def __init__(self, layer, pipeline_stage=None):
  @@ -146,13 +146,16 @@ class SequentialExtension(keras_extension_base.KerasExtensionBase): # pylint: d
  """Sets the asynchronous callbacks options when calling `fit()`, `evaluate()`
  and `predict()`.
  - When running `fit()`, `evaluate()` and `predict()` the callbacks the model
  - is configured with are executed after `steps_per_execution` have executed.
  + When running `fit()`, `evaluate()` and `predict()`, the callback functions
  + are called after executing the number of steps specified by
  + `steps_per_execution`, where each step processes one batch.
  +
  Enabling asynchronous callbacks means that the callbacks are invoked after
  every step, even when `steps_per_execution > 1`. This can reduce the latency
  - of receiving per step results and metrics at a cost of an extra thread
  + of receiving per-step results and metrics, at the cost of an extra thread
  running in the background of the application.
  - Note that this option is ignored for the `fit()` and `evaluate()` when
  +
  + Note that this option is ignored for `fit()` and `evaluate()` when
  running a pipelined model and `accumulate_outfeed=True` (configured via
  `set_pipelining_options`).
  