code: def get_config_class_from_processor_class(processor_class):
      """Get the config class from a processor class.
  
      Some config/model classes use tokenizers/feature_extractors from other models. For example, `GPT-J` uses
      `GPT2Tokenizer`. If no checkpoint is found for a config class, or a checkpoint is found without necessary file(s) to
      create the processor for `processor_class`, we get the config class that corresponds to `processor_class` and use it
      to find a checkpoint in order to create the processor.
      """
  
      processor_prefix = processor_class.__name__
      for postfix in [
          "TokenizerFast",
          "Tokenizer",
          "ImageProcessor",
          "FeatureExtractor",
          "Processor",
      ]:
          processor_prefix = processor_prefix.replace(postfix, "")
  
      # `Wav2Vec2CTCTokenizer` -> `Wav2Vec2Config`
      if processor_prefix == "Wav2Vec2CTC":
          processor_prefix = "Wav2Vec2"
  
      # Find the new configuration class
      new_config_name = f"{processor_prefix}Config"
      new_config_class = getattr(transformers_module, new_config_name)
  
      return new_config_class
  
imports: import argparse
  import collections.abc
  import copy
  import inspect
  import json
  import multiprocessing
  import os
  import shutil
  import tempfile
  import traceback
  from pathlib import Path
  from check_config_docstrings import get_checkpoint_from_config_class
  from datasets import load_dataset
  from get_test_info import get_model_to_tester_mapping, get_tester_classes_for_model
  from huggingface_hub import Repository, create_repo, hf_api, upload_folder
  from transformers import CONFIG_MAPPING, FEATURE_EXTRACTOR_MAPPING, IMAGE_PROCESSOR_MAPPING, PROCESSOR_MAPPING, TOKENIZER_MAPPING, AutoTokenizer, LayoutLMv3TokenizerFast, PreTrainedTokenizer, PreTrainedTokenizerFast, logging
  from transformers.feature_extraction_utils import FeatureExtractionMixin
  from transformers.file_utils import is_tf_available, is_torch_available
  from transformers.image_processing_utils import BaseImageProcessor
  from transformers.models.auto.configuration_auto import AutoConfig, model_type_to_module_name
  from transformers.models.fsmt import configuration_fsmt
  from transformers.processing_utils import ProcessorMixin, transformers_module
  from transformers.tokenization_utils_base import PreTrainedTokenizerBase
label_desc: Use image node_id 3 for calling a global function with example usage: get_config_class_from_processor_class(processor_class) and returns: new_config_class