code: def bn2d_inference_rule(n: Node, module_instance):
      """
      Given a BatchNorm2D instance and a node check the following conditions:
      - the input type can be expanded to a size 4 tensor: t =  (x_1, x_2, x_3, x_4)
      - the current node type can be expanded to a size 4 tensor: t' =  (x_1', x_2', x_3', x_4')
      - t is consistent with t'
      - x_2 is consistent with the module's num_features
      - x_2' is consistent with the module's num_features
      output type: the more precise type of t and 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
      n.type = expand_to_tensor_dim(n.type, 4)
  
      # we check the conditions on the incoming argument
      # and any existing annotation
      # we also check for consistency between both annotations
      if (
          is_consistent(
              arg_type.__args__[1], module_instance.num_features
          )
          and is_consistent(
              n.type.__args__[1], module_instance.num_features
          )
          and is_consistent(arg_type, n.type)
      ):
          # we choose the more precise type
          # to be the node type
          # so if an incoming argument has more type information
          # we set this node's type to be the argument type
          n.type = get_greatest_upper_bound(arg_type, n.type)
          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 10 for calling a global function with example usage: bn2d_inference_rule(n, module_instance) and returns: n