code: def get_ancestor_model(transformers_class_spec, model_config_path):
      """Load the model config from a file, configure the model, and return the model.
  
      This function returns the model class with all the pretrained weights. E.g., for BERT this is
      BertForPreTraining which includes masked language modeling and next sentence prediction heads.
  
      Args:
          transformers_class_spec (TransformersClassSpec): has refs to model, tokenizer, and config.
          model_config_path (str): Path to the JSON file containing the configuration parameters.
  
      Returns:
          Configured model.
  
      """
      config = transformers_class_spec.config_class.from_json_file(
          model_config_path
      )
      model = transformers_class_spec.model_class(config)
      return model
  
imports: from dataclasses import dataclass
  from typing import Any
  from typing import Dict
  from typing import List
  import torch
  import torch.nn
  import transformers
  import warnings
  import jiant.proj.main.components.container_setup
  import jiant.proj.main.modeling.primary
  import jiant.utils.python.strings
  from jiant.proj.main.modeling.heads import JiantHeadFactory
  from jiant.proj.main.modeling.taskmodels import JiantTaskModelFactory, Taskmodel, MLMModel
  from jiant.shared.model_resolution import ModelArchitectures
  from jiant.tasks.core import Task
label_desc: Use image node_id 10 for calling a global function with example usage: get_ancestor_model(transformers_class_spec, model_config_path) and returns: model