code: def _load_pretrained_hf_model_no_weights(
      modelClass: Type,
      pretrained_model_name_or_path: Optional[Union[str, os.PathLike]],
      **pretrained_kwargs,
  ):
      """Loads a HF model architecture without loading the weights."""
      from transformers import AutoConfig, AutoModel
  
      config = AutoConfig.from_pretrained(pretrained_model_name_or_path)
      return AutoModel.from_config(config), False
  
imports: import json
  import os
  from typing import Optional, Type, Union
  from unittest import mock
  import pytest
  import torch
  import ludwig.schema.encoders.utils
  from ludwig.api import LudwigModel
  from ludwig.constants import ENCODER, ENCODER_OUTPUT, MODEL_ECD, NAME, TEXT, TRAINER
  from ludwig.encoders import text_encoders
  from ludwig.error import ConfigValidationError
  from ludwig.globals import MODEL_HYPERPARAMETERS_FILE_NAME
  from ludwig.schema.model_config import ModelConfig
  from ludwig.utils.data_utils import load_json
  from ludwig.utils.torch_utils import get_torch_device
  from tests.integration_tests.parameter_update_utils import check_module_parameters_updated
  from tests.integration_tests.utils import category_feature, clear_huggingface_cache, generate_data, HF_ENCODERS, LocalTestBackend, text_feature
label_desc: Use image node_id 1 for calling a global function with example usage: _load_pretrained_hf_model_no_weights(modelClass, pretrained_model_name_or_path) and returns: AutoModel,  False