code: def convert_feature_extractor(feature_extractor, tiny_config):
      to_convert = False
      kwargs = {}
      if hasattr(tiny_config, "image_size"):
          kwargs["size"] = tiny_config.image_size
          kwargs["crop_size"] = tiny_config.image_size
          to_convert = True
      elif (
          hasattr(tiny_config, "vision_config")
          and tiny_config.vision_config is not None
          and hasattr(tiny_config.vision_config, "image_size")
      ):
          kwargs["size"] = tiny_config.vision_config.image_size
          kwargs["crop_size"] = tiny_config.vision_config.image_size
          to_convert = True
  
      # Speech2TextModel specific.
      if hasattr(tiny_config, "input_feat_per_channel"):
          kwargs["feature_size"] = tiny_config.input_feat_per_channel
          kwargs["num_mel_bins"] = tiny_config.input_feat_per_channel
          to_convert = True
  
      if to_convert:
          feature_extractor = feature_extractor.__class__(**kwargs)
  
      return feature_extractor
  
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 7 for calling a global function with example usage: convert_feature_extractor(feature_extractor, tiny_config) and returns: feature_extractor