code: def update_tiny_model_summary_file(report_path):
      with open(
          os.path.join(report_path, "tiny_model_summary.json")
      ) as fp:
          new_data = json.load(fp)
      with open("tests/utils/tiny_model_summary.json") as fp:
          data = json.load(fp)
      for key, value in new_data.items():
          if key not in data:
              data[key] = value
          else:
              for attr in [
                  "tokenizer_classes",
                  "processor_classes",
                  "model_classes",
              ]:
                  # we might get duplication here. We will remove them below when creating `updated_data`.
                  data[key][attr].extend(value[attr])
              new_sha = value.get("sha", None)
              if new_sha is not None:
                  data[key]["sha"] = new_sha
  
      updated_data = {}
      for key in sorted(data.keys()):
          updated_data[key] = {}
          for attr, value in data[key].items():
              # deduplication and sort
              updated_data[key][attr] = (
                  sorted(set(value)) if attr != "sha" else value
              )
  
      with open(
          os.path.join(report_path, "updated_tiny_model_summary.json"),
          "w",
      ) as fp:
          json.dump(updated_data, fp, indent=4, ensure_ascii=False)
  
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 20 for calling a global function with example usage: update_tiny_model_summary_file(report_path) without return types