(image, rank = 3)
Returns the dimensions of an image tensor.

  Args:
    image: A rank-D Tensor. For 3-D  of shape: `[height, width, channels]`.
    rank: The expected rank of the image

  Returns:
    A list of corresponding to the dimensions of the
    input image.  Dimensions that are statically known are python integers,
    otherwise they are integer scalar tensors.
Returns the dimensions of an image tensor.
Returns the dimensions of an image tensor .
def _ImageDimensions(image, rank = 3):
  """Returns the dimensions of an image tensor.

  Args:
    image: A rank-D Tensor. For 3-D  of shape: `[height, width, channels]`.
    rank: The expected rank of the image

  Returns:
    A list of corresponding to the dimensions of the
    input image.  Dimensions that are statically known are python integers,
    otherwise they are integer scalar tensors.
  """
  if image.get_shape().is_fully_defined():
    return image.get_shape().as_list()
  else:
    static_shape = image.get_shape().with_rank(rank).as_list()
    dynamic_shape = array_ops.unstack(array_ops.shape(image), rank)
    return [s if s is not None else d
            for s, d in zip(static_shape, dynamic_shape)]
def _ImageDimensions ( image , rank = 3 ) : if image . get_shape ( ) . is_fully_defined ( ) : return image . get_shape ( ) . as_list ( ) else : static_shape = image . get_shape ( ) . with_rank ( rank ) . as_list ( ) dynamic_shape = array_ops . unstack ( array_ops . shape ( image ) , rank ) return [ s if s is not None else d for s , d in zip ( static_shape , dynamic_shape ) ]