code: def element_wise_eq(n: Node):
      """
      For element-wise operations and handles broadcasting.
      Note that after applying broadcasting to the arguments
      we are able to determine if certain dimensions have not been broadcast
      if they are symbolicallu equal.
  
      in this case, we can establish equality between those dimensions and the
      corresponding output dimensions.
  
      Note that it takes two iterations for this result. One iteration to establish
      equality between certain dimensions of the operands (requiring the whole solver
      including unification) and another iteration to establish equality between the operands
      and the resulting type, requiring another round of constraint generation and unificaiton.
      """
      res = []
      if isinstance(n.args[0], Node) and isinstance(n.args[1], Node):
          arg_type1 = n.args[0].type
          arg_type2 = n.args[1].type
          if (
              isinstance(arg_type1, TensorType)
              and isinstance(arg_type2, TensorType)
              and isinstance(n.type, TensorType)
          ):
              args1, args2 = broadcast_types(arg_type1, arg_type2)
              # by this point, we know that args1 and args2 are the same size.
              a1 = args1.__args__
              a2 = args2.__args__
              a3 = n.type.__args__
  
              # we would be here in the second iteration where we establish equality
              # between operand type dimensions and the resulting type dimensions
              r = []
              for x, y, z in zip(a1, a2, a3):
                  if x == y:
                      r.append(Equality(x, z))
              res = r
      return res
  
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 27 for calling a global function with example usage: element_wise_eq(n) and returns: res