code: def retrieve_info_for_model(
      model_type, frameworks: Optional[List[str]] = None
  ):
      """
      Retrieves all the information from a given model_type.
  
      Args:
          model_type (`str`): A valid model type (like "bert" or "gpt2")
          frameworks (`List[str]`, *optional*):
              If passed, will only keep the info corresponding to the passed frameworks.
  
      Returns:
          `Dict`: A dictionary with the following keys:
          - **frameworks** (`List[str]`): The list of frameworks that back this model type.
          - **model_classes** (`Dict[str, List[str]]`): The model classes implemented for that model type.
          - **model_files** (`Dict[str, Union[Path, List[Path]]]`): The files associated with that model type.
          - **model_patterns** (`ModelPatterns`): The various patterns for the model.
      """
      if model_type not in auto_module.MODEL_NAMES_MAPPING:
          raise ValueError(f"{model_type} is not a valid model type.")
  
      model_name = auto_module.MODEL_NAMES_MAPPING[model_type]
      config_class = (
          auto_module.configuration_auto.CONFIG_MAPPING_NAMES[
              model_type
          ]
      )
      archive_map = auto_module.configuration_auto.CONFIG_ARCHIVE_MAP_MAPPING_NAMES.get(
          model_type, None
      )
      if (
          model_type
          in auto_module.tokenization_auto.TOKENIZER_MAPPING_NAMES
      ):
          tokenizer_classes = (
              auto_module.tokenization_auto.TOKENIZER_MAPPING_NAMES[
                  model_type
              ]
          )
          tokenizer_class = (
              tokenizer_classes[0]
              if tokenizer_classes[0] is not None
              else tokenizer_classes[1]
          )
      else:
          tokenizer_class = None
      feature_extractor_class = auto_module.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES.get(
          model_type, None
      )
      processor_class = (
          auto_module.processing_auto.PROCESSOR_MAPPING_NAMES.get(
              model_type, None
          )
      )
  
      model_files = get_model_files(model_type, frameworks=frameworks)
      model_camel_cased = config_class.replace("Config", "")
  
      available_frameworks = []
      for fname in model_files["model_files"]:
          if "modeling_tf" in str(fname):
              available_frameworks.append("tf")
          elif "modeling_flax" in str(fname):
              available_frameworks.append("flax")
          elif "modeling" in str(fname):
              available_frameworks.append("pt")
  
      if frameworks is None:
          frameworks = get_default_frameworks()
  
      frameworks = [f for f in frameworks if f in available_frameworks]
  
      model_classes = retrieve_model_classes(
          model_type, frameworks=frameworks
      )
  
      # Retrieve model upper-cased name from the constant name of the pretrained archive map.
      if archive_map is None:
          model_upper_cased = model_camel_cased.upper()
      else:
          parts = archive_map.split("_")
          idx = 0
          while idx < len(parts) and parts[idx] != "PRETRAINED":
              idx += 1
          if idx < len(parts):
              model_upper_cased = "_".join(parts[:idx])
          else:
              model_upper_cased = model_camel_cased.upper()
  
      model_patterns = ModelPatterns(
          model_name,
          checkpoint=find_base_model_checkpoint(
              model_type, model_files=model_files
          ),
          model_type=model_type,
          model_camel_cased=model_camel_cased,
          model_lower_cased=model_files["module_name"],
          model_upper_cased=model_upper_cased,
          config_class=config_class,
          tokenizer_class=tokenizer_class,
          feature_extractor_class=feature_extractor_class,
          processor_class=processor_class,
      )
  
      return {
          "frameworks": frameworks,
          "model_classes": model_classes,
          "model_files": model_files,
          "model_patterns": 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 transformers.models.auto
  from transformers.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 15 for calling a global function with example usage: retrieve_info_for_model(model_type, frameworks) and returns: dict