code: def merge_splits(
      match: Match,
      first_split_input: torch.fx.Node,
      first_split_sections: List[int],
      next_split_sections: List[int],
      # Note: dim is implicitly passed by TorchSplit, as it internally uses a pattern with dim
      dim: int,
  ):
      node = match.output_node()
      graph = match.graph
      first_split = node.args[0].args[0]
      next_split_index = node.args[0].args[1]
  
      new_split_sections = list(first_split_sections)
      new_split_sections[
          next_split_index : next_split_index + 1
      ] = next_split_sections
  
      first_split_dim = first_split.kwargs["dim"]
  
      to_remove = []
  
      with graph.inserting_before(first_split):
          # Add the new split node
          new_split = graph.call_function(
              torch.split,
              args=(first_split_input, new_split_sections),
              kwargs={"dim": first_split_dim},
          )
          first_split_num_to_user = {
              user.args[1]: user for user in first_split.users.keys()
          }
  
          new_split_num = 0
          for split_num in range(len(first_split_sections)):
              if split_num not in first_split_num_to_user:
                  new_split_num += 1
                  continue
              old_getitem = first_split_num_to_user[split_num]
              if split_num != next_split_index:
                  old_getitem.update_arg(0, new_split)
                  old_getitem.update_arg(1, new_split_num)
                  new_split_num += 1
              else:
                  next_split_num_to_user = {
                      user.args[1]: user for user in node.users.keys()
                  }
                  for next_split_num in range(len(next_split_sections)):
                      with graph.inserting_after(new_split):
                          new_getitem = graph.call_function(
                              operator.getitem,
                              args=(new_split, new_split_num),
                          )
                      new_split_num += 1
                      next_getitem = next_split_num_to_user[
                          next_split_num
                      ]
                      new_getitem.meta.update(next_getitem.meta)
                      next_getitem.replace_all_uses_with(new_getitem)
                      to_remove.append(next_getitem)
                  to_remove.append(node)
                  to_remove.append(old_getitem)
  
          to_remove.append(first_split)
      for node in to_remove:
          graph.erase_node(node)
  
      counters["inductor"]["consecutive_split_merged"] += 1
  
imports: import itertools
  import logging
  import operator
  from typing import Any, Callable, List, Optional, Sequence, Set, Tuple, Union
  from typing_extensions import TypeAlias
  import torch
  from torch._dynamo.utils import counters
  from ..pattern_matcher import Arg, CallFunction, CallFunctionVarArgs, CallMethodVarArgs, config_flag, FailedMatch, get_arg_value, Ignored, KeywordArg, ListOf, Match, MatchContext, MULTIPLE, PatternExpr, register_graph_pattern, RepeatedExpr
  from .pre_grad import merge_getitem_cat_pass, merge_splits_pass, normalization_pass, split_cat_pass, unbind_stack_pass
label_desc: Use image node_id 9 for calling a global function with example usage: merge_splits(match, first_split_input, first_split_sections, next_split_sections, dim) without return types