code: def matches_module_pattern(
      pattern: Iterable[Type], node: fx.Node, modules: Dict[str, Any]
  ):
      if len(node.args) == 0:
          return False
      nodes: Tuple[Any, fx.Node] = (node.args[0], node)
      for expected_type, current_node in zip(pattern, nodes):
          if not isinstance(current_node, fx.Node):
              return False
          if current_node.op != "call_module":
              return False
          if not isinstance(current_node.target, str):
              return False
          if current_node.target not in modules:
              return False
          if type(modules[current_node.target]) is not expected_type:
              return False
      return True
  
imports: import torch.fx
  from torch.fx.node import Argument, Target
  from torch.nn.utils.fusion import fuse_conv_bn_eval
  from typing import Type, Dict, Any, Tuple, Iterable, Optional, List, cast
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.fx.passes.shape_prop import ShapeProp
  import copy
  from collections import defaultdict
  import torch.utils.mkldnn
  import operator
  import time
  import logging
  from enum import Enum
label_desc: Use image node_id 2 for calling a global function with example usage: matches_module_pattern(pattern, node, modules) and returns: True, False, False, False, False, False, False