class_docstr: Helper class to simplify split-cat pattern. In simple cases, both split and cat node can be removed in a "split->cat"
  pattern. However, there are various cases where they can't and we need to simplify split/ add transforms before cat.
  Some such cases are:
      1. Final node has additional args (not coming from the initial split)
      2. Shuffling of args between split/cat
      3. Some final nodes are non-(cat/stack)
      4. Split-dim != cat-dim (but equal split)
  
  Note that any combination of the above cases can happen.
  
  To deal with 1, 2, & 3 - we iterate over all users of split. And figure out common "ranges" that can be merged.
  Then, we simplify the split accordingly. In the best case, split can be entirely removed.
  
  To deal with 4, we add some transformations (unflatten + movedim) (See `get_transform_params`).
  
  Finally, depending on final node being cat or stack, unsqueeze/flatten needs to be added.
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docstr: Replace the split node. It can either remove the split node if len(split_ranges) == 1, or simplify it
  into a split with lesser sections if len(split_ranges) > 1.
  
  Returns the new `user_inputs_list`, with tuples replaced with new getitems from the newer split node.
docstr_tok: ["Replace","the","split","node",".","It","can","either","remove","the","split","node","if","len","(","split_ranges",")","==","1",",","or","simplify","it","into","a","split","with","lesser","sections","if","len","(","split_ranges",")",">","1",".","Returns","the","new","`","user_inputs_list","`",",","with","tuples","replaced","with","new","getitems","from","the","newer","split","node","."]
code: def replace_split(
      self,
      graph: torch.fx.Graph,
      split_node: torch.fx.Node,
      split_sections: List[int],
      user_inputs_list: List[List[Union[torch.fx.Node, _Range]]],
      split_ranges: List[_Range],
  ) -> List[List[torch.fx.Node]]:
      """
      Replace the split node. It can either remove the split node if len(split_ranges) == 1, or simplify it
      into a split with lesser sections if len(split_ranges) > 1.
  
      Returns the new `user_inputs_list`, with tuples replaced with new getitems from the newer split node.
      """
      split_input = split_node.args[0]
      split_dim = split_node.kwargs["dim"]
      if (
          len(split_ranges) == 1
      ):  # We can completely eliminate the split node
          split_items = [split_input]
      else:
          with graph.inserting_after(split_node):
              new_split = graph.call_function(
                  torch.split,
                  args=(
                      split_input,
                      [r[1] - r[0] for r in split_ranges],
                  ),
                  kwargs={"dim": split_dim},
              )
              new_split.meta.update(split_node.meta)
              counters["inductor"]["scmerge_split_added"] += 1
          with graph.inserting_after(new_split):
              split_items = [
                  graph.call_function(
                      operator.getitem, args=(new_split, i)
                  )
                  for i in range(len(split_ranges))
              ]
      # Now assign the right getitem to the right input
      cumulative_sizes = [0] + torch.cumsum(
          torch.tensor(split_sections), 0
      ).tolist()
      new_user_inputs_list = []
      for user_inputs in user_inputs_list:
          new_user_inputs = []
          for user_input in user_inputs:
              if isinstance(user_input, tuple):
                  # Find the correct new getitem (present in split_items)
                  new_user_inputs.append(
                      split_items[
                          split_ranges.index(
                              (
                                  cumulative_sizes[user_input[0]],
                                  cumulative_sizes[user_input[1] + 1],
                              )
                          )
                      ]
                  )
              else:
                  new_user_inputs.append(user_input)
          new_user_inputs_list.append(new_user_inputs)
      return new_user_inputs_list
  
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 10 for calling the SplitCatSimplifier obj's underlying member method code with example usage: obj.replace_split(graph, split_node, split_sections, user_inputs_list, split_ranges) and returns: new_user_inputs_list