code: def build_model(model_arch, tiny_config, output_dir):
      """Create and save a model for `model_arch`.
  
      Also copy the set of processors to each model (under the same model type) output folder.
      """
  
      checkpoint_dir = get_checkpoint_dir(output_dir, model_arch)
  
      processor_output_dir = os.path.join(output_dir, "processors")
      # copy the (same set of) processors (for a model type) to the model arch. specific folder
      if os.path.isdir(processor_output_dir):
          shutil.copytree(
              processor_output_dir, checkpoint_dir, dirs_exist_ok=True
          )
  
      tiny_config = copy.deepcopy(tiny_config)
  
      if any(
          model_arch.__name__.endswith(x)
          for x in ["ForCausalLM", "LMHeadModel"]
      ):
          tiny_config.is_encoder_decoder = False
          tiny_config.is_decoder = True
  
      model = model_arch(config=tiny_config)
      model.save_pretrained(checkpoint_dir)
      model.from_pretrained(checkpoint_dir)
  
      return model
  
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 10 for calling a global function with example usage: build_model(model_arch, tiny_config, output_dir) and returns: model