(image, output_height, output_width, is_training=False,
                     resize_side_min=_RESIZE_SIDE_MIN,
                     resize_side_max=_RESIZE_SIDE_MAX)
Preprocesses the given image.

  Args:
    image: A `Tensor` representing an image of arbitrary size.
    output_height: The height of the image after preprocessing.
    output_width: The width of the image after preprocessing.
    is_training: `True` if we're preprocessing the image for training and
      `False` otherwise.
    resize_side_min: The lower bound for the smallest side of the image for
      aspect-preserving resizing. If `is_training` is `False`, then this value
      is used for rescaling.
    resize_side_max: The upper bound for the smallest side of the image for
      aspect-preserving resizing. If `is_training` is `False`, this value is
      ignored. Otherwise, the resize side is sampled from
        [resize_size_min, resize_size_max].

  Returns:
    A preprocessed image.
Preprocesses the given image.
Preprocesses the given image .
def preprocess_image(image, output_height, output_width, is_training=False,
                     resize_side_min=_RESIZE_SIDE_MIN,
                     resize_side_max=_RESIZE_SIDE_MAX):
  """Preprocesses the given image.

  Args:
    image: A `Tensor` representing an image of arbitrary size.
    output_height: The height of the image after preprocessing.
    output_width: The width of the image after preprocessing.
    is_training: `True` if we're preprocessing the image for training and
      `False` otherwise.
    resize_side_min: The lower bound for the smallest side of the image for
      aspect-preserving resizing. If `is_training` is `False`, then this value
      is used for rescaling.
    resize_side_max: The upper bound for the smallest side of the image for
      aspect-preserving resizing. If `is_training` is `False`, this value is
      ignored. Otherwise, the resize side is sampled from
        [resize_size_min, resize_size_max].

  Returns:
    A preprocessed image.
  """
  if is_training:
    return preprocess_for_train(image, output_height, output_width,
                                resize_side_min, resize_side_max)
  else:
    return preprocess_for_eval(image, output_height, output_width,
                               resize_side_min)
def preprocess_image ( image , output_height , output_width , is_training = False , resize_side_min = _RESIZE_SIDE_MIN , resize_side_max = _RESIZE_SIDE_MAX ) : if is_training : return preprocess_for_train ( image , output_height , output_width , resize_side_min , resize_side_max ) else : return preprocess_for_eval ( image , output_height , output_width , resize_side_min )