code: def broadcast_like(
      F: ModuleType, x: nd_sym_type, y: nd_sym_type
  ) -> nd_sym_type:
      """
      Implementation of broadcast_like using broadcast_add and broadcast_mul because ONNX doesn't support broadcast_like.
      :param F: backend api, either `mxnet.nd` or `mxnet.sym` (if block has been hybridized).
      :param x: input to be broadcast
      :param y: tensor to broadcast x like
      :return: broadcast x
      """
      return F.broadcast_mul(x, (y * 0) + 1)
  
imports: import inspect
  from typing import Any, Dict, Generator, Iterable, List, Tuple, Union
  from types import ModuleType
  import mxnet
  from mxnet import gluon, nd
  from mxnet.ndarray import NDArray
  import numpy
  from rl_coach.core_types import GradientClippingMethod
label_desc: Use image node_id 13 for calling a global function with example usage: broadcast_like(F, x, y) and returns: F