code: def is_broadcast_compatible(
      shape_x: tf.TensorShape, shape_y: tf.TensorShape
  ) -> bool:
      """Returns True if `shape_x` and `shape_y` are broadcast compatible.
  
      Args:
        shape_x: A `TensorShape`.
        shape_y: A `TensorShape`.
  
      Returns:
        True if a shape exists that both `shape_x` and `shape_y` can be broadcasted
        to. False otherwise.
      """
      if shape_x.ndims is None or shape_y.ndims is None:
          return False
      return _broadcast_shape_helper(shape_x, shape_y) is not None
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import itertools
  from typing import Any, List, Optional, Tuple, Union
  import numpy
  import six
  from six.moves import range
  from six.moves import zip
  import tensorflow
label_desc: Use image node_id 2 for calling a global function with example usage: is_broadcast_compatible(shape_x, shape_y) and returns: unknown, False