code: def setup_jiant_model(
      hf_pretrained_model_name_or_path: str,
      model_config_path: str,
      task_dict: Dict[str, Task],
      taskmodels_config: container_setup.TaskmodelsConfig,
  ):
      """Sets up tokenizer, encoder, and task models, and instantiates and returns a JiantModel.
  
      Args:
          hf_pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`):
              Can be either:
  
                  - A string, the `model id` of a predefined tokenizer hosted inside a model
                    repo on huggingface.co. Valid model ids can be located at the root-level,
                    like ``bert-base-uncased``, or namespaced under
                    a user or organization name, like ``dbmdz/bert-base-german-cased``.
                  - A path to a `directory` containing vocabulary files required by the
                    tokenizer, for instance saved using the
                    :func:`~transformers.PreTrainedTokenizer.save_pretrained` method, e.g.,
                    ``./my_model_directory/``.
                  - A path or url to a single saved vocabulary file if and only if
                    the tokenizer only requires a single vocabulary file (like Bert or XLNet),
                    e.g.: ``./my_model_directory/vocab.txt``. (Not
                    applicable to all derived classes)
          model_config_path (str): Path to the JSON file containing the configuration parameters.
          task_dict (Dict[str, tasks.Task]): map from task name to task instance.
          taskmodels_config: maps mapping from tasks to models, and specifying task-model configs.
  
      Returns:
          JiantModel nn.Module.
  
      """
      hf_model = transformers.AutoModel.from_pretrained(
          hf_pretrained_model_name_or_path
      )
      tokenizer = transformers.AutoTokenizer.from_pretrained(
          hf_pretrained_model_name_or_path, use_fast=False
      )
      encoder = primary.JiantTransformersModelFactory()(hf_model)
      taskmodels_dict = {
          taskmodel_name: create_taskmodel(
              task=task_dict[task_name_list[0]],  # Take the first task
              encoder=encoder,
              taskmodel_kwargs=taskmodels_config.get_taskmodel_kwargs(
                  taskmodel_name
              ),
          )
          for taskmodel_name, task_name_list in get_taskmodel_and_task_names(
              taskmodels_config.task_to_taskmodel_map
          ).items()
      }
      return primary.JiantModel(
          task_dict=task_dict,
          encoder=encoder,
          taskmodels_dict=taskmodels_dict,
          task_to_taskmodel_map=taskmodels_config.task_to_taskmodel_map,
          tokenizer=tokenizer,
      )
  
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 1 for calling a global function with example usage: setup_jiant_model(hf_pretrained_model_name_or_path, model_config_path, task_dict, taskmodels_config) and returns: primary