code: def maxpool2d_inference_rule(n: Node, module_instance):
      """
      Given a MaxPool2D instance and a node check the following conditions:
      - Input size matches size 3 or 4
      - Current node type is consistent with the output type we will calculate
      - Input size matches output size and the last two dimensions of the output
        are w_out and h_out. The remaining dimensions are the same as the input
      - Our final result is the greatest upper bound of the output we calculate
        and the current node type.
      """
      assert isinstance(n.args[0], Node)
  
      if n.args[0].type == Dyn and isinstance(n.type, TensorType):
          n.args[0].type = expand_to_tensor_dim(
              n.args[0].type, len(n.type.__args__)
          )
      if isinstance(n.args[0].type, TensorType):
          output = maxpool2d_check(n.args[0].type, module_instance)
          n.type = get_greatest_upper_bound(output, n.type)
      return n.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 16 for calling a global function with example usage: maxpool2d_inference_rule(n, module_instance) and returns: n