parameters: (op)
return_statement: return [tensor_shape.TensorShape(
        [input_shape[0], height, width, input_shape[3]])]
docstring: Shape function for the resize_bilinear and resize_nearest_neighbor ops.
docstring_summary: Shape function for the resize_bilinear and resize_nearest_neighbor ops.
docstring_tokens: Shape function for the resize_bilinear and resize_nearest_neighbor ops .
function: def _ResizeShape(op):
    """Shape function for the resize_bilinear and resize_nearest_neighbor ops."""
    input_shape = op.inputs[0].get_shape().with_rank(4)
    unused_size_shape = op.inputs[1].get_shape().merge_with([2])
    size = tensor_util.constant_value(op.inputs[1])
    if size is not None:
      height = size[0]
      width = size[1]
    else:
      height = None
      width = None
    return [tensor_shape.TensorShape(
        [input_shape[0], height, width, input_shape[3]])]
function_tokens: def _ResizeShape ( op ) : input_shape = op . inputs [ 0 ] . get_shape ( ) . with_rank ( 4 ) unused_size_shape = op . inputs [ 1 ] . get_shape ( ) . merge_with ( [ 2 ] ) size = tensor_util . constant_value ( op . inputs [ 1 ] ) if size is not None : height = size [ 0 ] width = size [ 1 ] else : height = None width = None return [ tensor_shape . TensorShape ( [ input_shape [ 0 ] , height , width , input_shape [ 3 ] ] ) ]