code: def conv2d_inference_rule(n: Node, module_instance):
      """
      Given a Conv2D instance and a node check the following conditions:
      - the input type can be expanded to a size 4 tensor: t =  (x_1, x_2, H, W)
      - the current node type can be expanded to a size 4 tensor: t' =  (x_1', x_2', x_3', x_4')
      - x_2 is consistent with the module's in_channels
      - let o = (x_1, out_channels, H_out, W_out)
      then the output is the greatest upper bound of o and the existing node type t'.
      """
      assert isinstance(n.args[0], Node)
      n.args[0].type = expand_to_tensor_dim(n.args[0].type, 4)
      arg_type = n.args[0].type
      curr_node_type = expand_to_tensor_dim(n.type, 4)
  
      if is_consistent(
          arg_type.__args__[1], module_instance.in_channels
      ):
          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(
              (
                  arg_type.__args__[0],
                  module_instance.out_channels,
                  h_out,
                  w_out,
              )
          )
          gub = get_greatest_upper_bound(new_type, curr_node_type)
          n.type = gub
          return n.type
      else:
          raise TypeError(
              f"Cannot apply {module_instance} with input type { arg_type} and existing type {n.type} on {n}"
          )
  
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 13 for calling a global function with example usage: conv2d_inference_rule(n, module_instance) and returns: n