code: def _reroute_tuple_getitem_pattern(graph: Graph):
      """
      Search for patterns where N consecutive `tuple` call_function nodes are followed by
      N consecutive `getitem` call_function nodes that are "reverses" of the `tuple` nodes.
      If we find this pattern, reroute the consumers of the last `getitem` to skip these
      N `tuple` and `getitem` nodes.
  
      Before:
  
          a   b     c
          |   \\   /
          \\   tuple
           \\   /
            tuple
              |
          getitem(1)
              |
          getitem(0)
              |
              d
  
      After:
  
          b
          |
          d
      """
  
      def find_patterns(
          node: Node,
          index_stack: List[int],
          current_pattern: List[Node],
          matched_patterns: List[List[Node]],
          seen: Set[Tuple[Node, Tuple[int, ...]]],
      ):
          """
          Traverse the graph recursively to match for the N-tuple - N-getitem patterns,
          starting at the given node.
  
          We use a stack to keep track of the expected `getitem` indices, since these are
          reversed from the `tuple` indices. In the above example, the stack after
          (b -> tuple -> tuple) will be [0, 1], which will be popped by getitem(1) first
          and then by getitem(0).
  
          TODO: traverse upwards from the output and handle the case when tuple is not a
          separate node, e.g. graph.call_function(operator.getitem, args=(a, (b, c)))
          """
          if len(index_stack) == 0 and len(current_pattern) > 0:
              matched_patterns.append(copy.copy(current_pattern))
              current_pattern.clear()
  
          # Avoid duplicating work
          state = (node, tuple(index_stack))
          if state in seen:
              return
          seen.add(state)
  
          # Iterate through users of this node to find tuple/getitem nodes to match
          for user in node.users:
              if user.op == "call_function" and user.target == tuple:
                  for i, user_arg in enumerate(user.args[0]):  # type: ignore[arg-type]
                      if user_arg == node:
                          index_stack.append(i)
                          current_pattern.append(user)
                          find_patterns(
                              user,
                              index_stack,
                              current_pattern,
                              matched_patterns,
                              seen,
                          )
              elif (
                  user.op == "call_function"
                  and user.target == operator.getitem
              ):
                  if len(index_stack) > 0:
                      if user.args[1] == index_stack[-1]:
                          index_stack.pop()
                          current_pattern.append(user)
                          find_patterns(
                              user,
                              index_stack,
                              current_pattern,
                              matched_patterns,
                              seen,
                          )
          return matched_patterns
  
      # Collect all matched patterns
      matched_patterns: List[List[Node]] = []
      seen: Set[
          Tuple[Node, Tuple[int, ...]]
      ] = set()  # (node, index_stack)
      for node in graph.nodes:
          find_patterns(node, [], [], matched_patterns, seen)
  
      # For each pattern, redirect all consumers of the last getitem node to the correct input
      # of the first tuple node
      for pattern in matched_patterns:
          first_tuple = pattern[0]
          last_getitem = pattern[-1]
          assert (
              first_tuple.op == "call_function"
              and first_tuple.target == tuple
          )
          assert (
              last_getitem.op == "call_function"
              and last_getitem.target == operator.getitem
          )
          last_getitem_index = last_getitem.args[1]
          new_input = first_tuple.args[0][last_getitem_index]  # type: ignore[index]
          for user in list(last_getitem.users.keys()):
              user.replace_input_with(last_getitem, new_input)
  
imports: import copy
  import torch
  import torch.nn
  from torch.ao.quantization import QConfigAny, QuantType
  from torch.ao.quantization.backend_config import DTypeWithConstraints
  from torch.ao.quantization.fake_quantize import FakeQuantizeBase, FixedQParamsFakeQuantize
  from torch.ao.quantization.observer import FixedQParamsObserver, ObserverBase
  from torch.ao.quantization.qconfig import float16_static_qconfig, float16_dynamic_qconfig, qconfig_equals
  from torch.ao.quantization.stubs import DeQuantStub
  from torch.ao.quantization.utils import activation_is_statically_quantized
  from torch.ao.quantization.observer import _is_activation_post_process
  from torch.ao.quantization.qconfig_mapping import QConfigMapping
  from torch.fx import GraphModule, map_arg
  from torch.fx.graph import Graph, Node
  from .custom_config import PrepareCustomConfig
  from ._decomposed import quantized_decomposed_lib
  from typing import Callable, Optional, List, Dict, Any, Set, Tuple, Union, Type
  from dataclasses import dataclass
  from collections import namedtuple
  import operator
  import warnings
label_desc: Use image node_id 24 for calling a global function with example usage: _reroute_tuple_getitem_pattern(graph) and returns: matched_patterns