code: def merge_getitem_cat(
      match: Match, split_sections: List[int], dim: int
  ):
      if not isinstance(
          split_sections, (list, tuple)
      ):  # Unnormalized split
          return
      graph = match.graph
      split_node = next(
          node for node in match.nodes if node.target == torch.split
      )
      split_input, split_size, split_dim = _get_split_args_default(
          split_node
      )
      # if the cat and split have different dims, return
      # Find the next users (i.e. users after the getitem)
      next_users = find_next_users(split_node)
      # 'immutable_list' object does not support mutation. Create a new copy of it
      split_sections = list(split_sections)
      for cat_user in next_users:
          if cat_user.target == torch.cat:
              cat_dim = get_arg_value(cat_user, 1, "dim")
              if split_dim != cat_dim:
                  continue
              # check the all getitems in the cat_user from the same node
              if not safe_to_abort_node(cat_user):
                  continue
              # find the index of getitems to be cated/stacked
              indices = []
              for arg in cat_user.args[0]:
                  indices.append(arg.args[1])
              # indices may not be necessarily sorted, we sort them first
              indices.sort()
              # the gettitems to be merged must be consecutive, otherwise
              # returned sliced tensor could be wrong
              if indices[len(indices) - 1] - indices[0] + 1 != len(
                  indices
              ):
                  continue
              # update the arg of cat user, only keep the first getitem
              cat_user.update_arg(0, cat_user.args[0][0])
              # calculate the fused tensor sizes in the indices
              fused_tensor_size = 0
              for i in range(len(split_node.args[1])):
                  if i in indices:
                      fused_tensor_size += split_node.args[1][i]
              # update the split sections
              split_sections[indices[0]] = fused_tensor_size
              # padding others with zeros to keep the same dict size
              for i in indices[1:]:
                  split_sections[i] = 0
              # remove all unused indexes in the split_node
              new_split_sections, index_mapping = remove_zeros(
                  split_sections
              )
              with graph.inserting_after(split_node):
                  new_split_node = graph.call_function(
                      torch.split,
                      args=(split_input, split_sections),
                      kwargs={"dim": split_dim},
                  )
                  split_node.replace_all_uses_with(new_split_node)
                  new_split_node.meta.update(split_node.meta)
                  # remove all unused getitem nodes
                  to_remove = [cat_user]
                  # dictionary keys changed during iteration
                  new_split_getitem_nodes = list(
                      new_split_node.users.keys()
                  )
                  for getitem_node in new_split_getitem_nodes:
                      if getitem_node.args[1] in indices[1:]:
                          to_remove.append(getitem_node)
                      # update meta data of getitem
                      elif getitem_node.args[1] == indices[0]:
                          cat_user.replace_all_uses_with(getitem_node)
                          getitem_node.meta.update(cat_user.meta)
                      else:
                          # update getitem index for new split node
                          getitem_node.update_arg(
                              1, index_mapping[getitem_node.args[1]]
                          )
                  graph.erase_node(split_node)
                  for getitem_node in to_remove:
                      graph.erase_node(getitem_node)
                  # update the split sections of new split node
                  new_split_node.update_arg(1, new_split_sections)
                  split_node = new_split_node
                  split_sections = new_split_sections
  
                  counters["inductor"]["getitem_cat_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 15 for calling a global function with example usage: merge_getitem_cat(match, split_sections, dim) without return types