message: Update mobilenet_v3.preprocess_input docstring to reflect its real behavior.
  Fix
diff: @@ -77,11 +77,6 @@ BASE_DOCSTRING = """Instantiates the {name} architecture.
  Optionally loads weights pre-trained on ImageNet.
  - Note: each Keras Application expects a specific kind of input preprocessing.
  - For MobileNetV3, call
  - `tf.keras.applications.mobilenet_v3.preprocess_input` on your
  - inputs before passing them to the model.
  -
  Arguments:
  input_shape: Optional shape tuple, to be specified if you would
  like to use a model with an input image resolution that is not
  @@ -136,6 +131,10 @@ BASE_DOCSTRING = """Instantiates the {name} architecture.
  on the "top" layer. Ignored unless `include_top=True`. Set
  `classifier_activation=None` to return the logits of the "top" layer.
  + Call arguments:
  + inputs: A floating point `numpy.array` or a `tf.Tensor`, 4D with 3 color
  + channels, with values in the range [0, 255].
  +
  Returns:
  A `keras.Model` instance.
  @@ -555,6 +554,24 @@ def _inverted_res_block(x, expansion, filters, kernel_size, stride, se_ratio,
  @keras_export('keras.applications.mobilenet_v3.preprocess_input')
  def preprocess_input(x, data_format=None): # pylint: disable=unused-argument
  + """A placeholder method for backward compatibility.
  +
  + The preprocessing logic has been included in the mobilenet_v3 model
  + implementation. Users are no longer required to call this method to normalize
  + the input data. This method does nothing and only kept as a placeholder to
  + align the API surface between old and new version of model.
  +
  + Args:
  + x: A floating point `numpy.array` or a `tf.Tensor`.
  + data_format: Optional data format of the image tensor/array. Defaults to
  + None, in which case the global setting
  + `tf.keras.backend.image_data_format()` is used (unless you changed it,
  + it defaults to "channels_last").{mode}
  +
  + Returns:
  + Unchanged `numpy.array` or `tf.Tensor`.
  + """
  +
  return x
  @@ -563,8 +580,4 @@ def decode_predictions(preds, top=5):
  return imagenet_utils.decode_predictions(preds, top=top)
  -preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format(
  - mode='',
  - ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TF,
  - error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC)
  decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__
  