code: def conv_rule(n: Node, module_instance):
      """
      Represents the outout in terms of an algrbraic expression w.r.t
      the input when possible
      """
      assert isinstance(n.args[0], Node)
      arg_type = n.args[0].type
      if isinstance(arg_type, TensorType) and isinstance(
          n.type, TensorType
      ):
          w_in = arg_type.__args__[3]
          h_in = arg_type.__args__[2]
          h_out = calculate_out_dimension(h_in, module_instance, 0)
          w_out = calculate_out_dimension(w_in, module_instance, 1)
          new_type = TensorType(
              (n.type.__args__[0], n.type.__args__[1], h_out, w_out)
          )
          n.type = new_type
          return new_type
  
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 29 for calling a global function with example usage: conv_rule(n, module_instance) and returns: new_type