code: def get_architectures_from_config_class(
      config_class, arch_mappings, models_to_skip=None
  ):
      """Return a tuple of all possible architectures attributed to a configuration class `config_class`.
  
      For example, BertConfig -> [BertModel, BertForMaskedLM, ..., BertForQuestionAnswering].
      """
      # A model architecture could appear in several mappings. For example, `BartForConditionalGeneration` is in
      #   - MODEL_FOR_PRETRAINING_MAPPING_NAMES
      #   - MODEL_WITH_LM_HEAD_MAPPING_NAMES
      #   - MODEL_FOR_MASKED_LM_MAPPING_NAMES
      #   - MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
      # We avoid the duplication.
      architectures = set()
  
      if models_to_skip is None:
          models_to_skip = []
      models_to_skip = UNCONVERTIBLE_MODEL_ARCHITECTURES.union(
          models_to_skip
      )
  
      for mapping in arch_mappings:
          if config_class in mapping:
              models = mapping[config_class]
              models = (
                  tuple(models)
                  if isinstance(models, collections.abc.Sequence)
                  else (models,)
              )
              for model in models:
                  if model.__name__ not in models_to_skip:
                      architectures.add(model)
  
      architectures = tuple(architectures)
  
      return architectures
  
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 2 for calling a global function with example usage: get_architectures_from_config_class(config_class, arch_mappings, models_to_skip) and returns: architectures