code: def add_model_to_auto_classes(
      old_model_patterns: ModelPatterns,
      new_model_patterns: ModelPatterns,
      model_classes: Dict[str, List[str]],
  ):
      """
      Add a model to the relevant mappings in the auto module.
  
      Args:
          old_model_patterns (`ModelPatterns`): The patterns for the old model.
          new_model_patterns (`ModelPatterns`): The patterns for the new model.
          model_classes (`Dict[str, List[str]]`): A dictionary framework to list of model classes implemented.
      """
      for filename in AUTO_CLASSES_PATTERNS:
          # Extend patterns with all model classes if necessary
          new_patterns = []
          for pattern in AUTO_CLASSES_PATTERNS[filename]:
              if re.search("any_([a-z]*)_class", pattern) is not None:
                  framework = re.search(
                      "any_([a-z]*)_class", pattern
                  ).groups()[0]
                  if framework in model_classes:
                      new_patterns.extend(
                          [
                              pattern.replace(
                                  "{" + f"any_{framework}_class" + "}",
                                  cls,
                              )
                              for cls in model_classes[framework]
                          ]
                      )
              elif "{config_class}" in pattern:
                  new_patterns.append(
                      pattern.replace(
                          "{config_class}",
                          old_model_patterns.config_class,
                      )
                  )
              elif "{image_processor_class}" in pattern:
                  if (
                      old_model_patterns.image_processor_class
                      is not None
                      and new_model_patterns.image_processor_class
                      is not None
                  ):
                      new_patterns.append(
                          pattern.replace(
                              "{image_processor_class}",
                              old_model_patterns.image_processor_class,
                          )
                      )
              elif "{feature_extractor_class}" in pattern:
                  if (
                      old_model_patterns.feature_extractor_class
                      is not None
                      and new_model_patterns.feature_extractor_class
                      is not None
                  ):
                      new_patterns.append(
                          pattern.replace(
                              "{feature_extractor_class}",
                              old_model_patterns.feature_extractor_class,
                          )
                      )
              elif "{processor_class}" in pattern:
                  if (
                      old_model_patterns.processor_class is not None
                      and new_model_patterns.processor_class is not None
                  ):
                      new_patterns.append(
                          pattern.replace(
                              "{processor_class}",
                              old_model_patterns.processor_class,
                          )
                      )
              else:
                  new_patterns.append(pattern)
  
          # Loop through all patterns.
          for pattern in new_patterns:
              full_name = (
                  TRANSFORMERS_PATH / "models" / "auto" / filename
              )
              old_model_line = pattern
              new_model_line = pattern
              for attr in ["model_type", "model_name"]:
                  old_model_line = old_model_line.replace(
                      "{" + attr + "}",
                      getattr(old_model_patterns, attr),
                  )
                  new_model_line = new_model_line.replace(
                      "{" + attr + "}",
                      getattr(new_model_patterns, attr),
                  )
              if "pretrained_archive_map" in pattern:
                  old_model_line = old_model_line.replace(
                      "{pretrained_archive_map}",
                      f"{old_model_patterns.model_upper_cased}_PRETRAINED_CONFIG_ARCHIVE_MAP",
                  )
                  new_model_line = new_model_line.replace(
                      "{pretrained_archive_map}",
                      f"{new_model_patterns.model_upper_cased}_PRETRAINED_CONFIG_ARCHIVE_MAP",
                  )
  
              new_model_line = new_model_line.replace(
                  old_model_patterns.model_camel_cased,
                  new_model_patterns.model_camel_cased,
              )
  
              add_content_to_file(
                  full_name, new_model_line, add_after=old_model_line
              )
  
      # Tokenizers require special handling
      insert_tokenizer_in_auto_module(
          old_model_patterns, new_model_patterns
      )
  
imports: import difflib
  import json
  import os
  import re
  from argparse import ArgumentParser, Namespace
  from dataclasses import dataclass
  from datetime import date
  from itertools import chain
  from pathlib import Path
  from typing import Any, Callable, Dict, List, Optional, Pattern, Tuple, Union
  import yaml
  from ..models import auto
  from ..models.auto.configuration_auto import model_type_to_module_name
  from ..utils import is_flax_available, is_tf_available, is_torch_available, logging
  from .None import BaseTransformersCLICommand
label_desc: Use image node_id 21 for calling a global function with example usage: add_model_to_auto_classes(old_model_patterns, new_model_patterns, model_classes) without return types