code: def aten_shape_broadcast(
      fn: Any, x: ShapeTensor, y: ShapeTensor, **kwargs
  ) -> MutableShape:
      # Rules are:
      # 1. If the number of dimensions of x and y are not equal,
      #    prepend 1 to the dimensions of the tensor with fewer dimensions to make them equal length.
      # 2. Merge the shape of x and y. Avoid 1.
  
      if y.real_shape is None:
          return cast(MutableShape, x.real_shape)  # Assume x has shape.
      if x.real_shape is None:
          return cast(MutableShape, y.real_shape)
  
      x_shape = list(x.real_shape)
      y_shape = list(y.real_shape)
  
      if len(x_shape) > len(y_shape):
          y_shape = [1] * (len(x_shape) - len(y_shape)) + y_shape  # type: ignore
      elif len(x_shape) < len(y_shape):
          x_shape = [1] * (len(y_shape) - len(x_shape)) + x_shape  # type: ignore
      assert len(x_shape) == len(y_shape)
  
      # Use "is" here to avoid generating mutable expressions.
      ONE = 1
      return MutableShape(
          *[y if x is ONE else x for x, y in zip(x_shape, y_shape)]
      )
  
imports: from __future__ import annotations
  import logging
  import warnings
  from typing import Callable, Type, Tuple, Any, cast
  import torch
  from torch import nn
  import nni.nas.nn.pytorch
  from nni.mutable import MutableExpression
  from .shape import Formula, ShapeTensor, MutableShape, extract_shape_info, switch_case_shape_info, shape_inference
  from ._attrs import _getattr, tuple_2_t
label_desc: Use image node_id 19 for calling a global function with example usage: aten_shape_broadcast(fn, x, y) and returns: MutableShape, cast, cast