code: def broadcast_types(t1, t2):
      """
      Applies broadcasting to both given types such that they
      become consistent with eachother and returns two new
      resulting types
      """
  
      # if either type is Dyn, do nothing since the types are already consistent
      if (
          t1 == Dyn
          or t2 == Dyn
          or isinstance(t1, Var)
          or isinstance(t2, Var)
      ):
          return t1, t2
  
      if isinstance(t1, TensorType) and isinstance(t2, TensorType):
          s1 = len(t1.__args__)
          s2 = len(t2.__args__)
  
          new_t1 = list(t1.__args__)
          new_t2 = list(t2.__args__)
  
          # We make the types the same length which is the first requirement
          # for consistency
          if s1 > s2:
              for i in range(s1 - s2):
                  new_t2.insert(0, 1)
  
          elif s2 > s1:
              for i in range(s2 - s1):
                  new_t1.insert(0, 1)
  
          # we replace occurrences of "1" with each tensor with
          # the corresponding type from the other tensor
          for i, (x, y) in enumerate(zip(new_t1, new_t2)):
              if x == 1:
                  new_t1[i] = y
              elif y == 1:
                  new_t2[i] = x
  
          # at this point our tensors should be consistent
          # and we can apply the element-wise operation and find the right dimension
          # for the output of the operation
          (t1, t2) = TensorType(tuple(new_t1)), TensorType(
              tuple(new_t2)
          )
          return (t1, t2)
      else:
          raise TypeError(f"Cannot broadcast types {t1} and {t2}")
  
imports: from functools import reduce
  import torch
  import operator
  from torch.fx.tensor_type import Dyn, is_consistent, TensorType, is_more_precise
  from typing import Callable, Dict
  from torch.fx.node import Target, Node
  from torch.nn.modules.batchnorm import BatchNorm2d
  from torch.nn.modules.conv import Conv2d
  from torch.fx.experimental.refinement_types import Equality
  import itertools
  from torch.fx.experimental.unification import Var
  import sympy
label_desc: Use image node_id 2 for calling a global function with example usage: broadcast_types(t1, t2) and returns: t1,  t2, t1,  t2