code: def create_jiant_task_container(
      task_config_path_dict: Dict,
      task_cache_config_dict: Dict,
      sampler_config: Dict,
      global_train_config: Dict,
      task_specific_configs_dict: Dict,
      metric_aggregator_config: Dict,
      taskmodels_config: Dict,
      task_run_config: Dict,
      verbose: bool = True,
  ) -> JiantTaskContainer:
      """Read and interpret config files, initialize configuration objects, return JiantTaskContainer.
  
      Args:
          task_config_path_dict (Dict[str, str]): map of task names to task config files.
          task_cache_config_dict (Dict[str, str]): map of task names to cache file dirs.
          sampler_config (Dict): map containing sample config options, e.g., uniform task sampling.
          global_train_config (Dict): map of training configs shared by all tasks (e.g., max_steps).
          task_specific_configs_dict (Dict): map of maps mapping task names to task-specific options.
          metric_aggregator_config (Dict): map containing task metric aggregation options.
          taskmodels_config: maps mapping from tasks to models, and specifying task-model configs.
          task_run_config: config determining which tasks are used in which phase (e.g., train).
          verbose: True to print task info.
  
      Returns:
          JiantTaskContainer carrying components configured and set up pre-runner.
  
      """
      task_dict = create_task_dict(
          task_config_dict=task_config_path_dict, verbose=verbose
      )
      task_cache_dict = create_task_cache_dict(
          task_cache_config_dict=task_cache_config_dict
      )
      global_train_config = GlobalTrainConfig.from_dict(
          global_train_config
      )
      task_specific_config = create_task_specific_configs(
          task_specific_configs_dict=task_specific_configs_dict,
      )
      taskmodels_config = TaskmodelsConfig.from_dict(taskmodels_config)
      task_run_config = TaskRunConfig.from_dict(task_run_config)
  
      num_train_examples_dict = get_num_train_examples(
          task_cache_dict=task_cache_dict,
          train_task_list=task_run_config.train_task_list,
      )
      task_sampler = jiant_task_sampler.create_task_sampler(
          sampler_config=sampler_config,
          # task sampler samples only from the training tasks
          task_dict={
              task_name: task_dict[task_name]
              for task_name in task_run_config.train_task_list
          },
          task_to_num_examples_dict=num_train_examples_dict,
      )
      metric_aggregator = jiant_task_sampler.create_metric_aggregator(
          metric_aggregator_config=metric_aggregator_config,
      )
      return JiantTaskContainer(
          task_dict=task_dict,
          task_sampler=task_sampler,
          global_train_config=global_train_config,
          task_cache_dict=task_cache_dict,
          task_specific_configs=task_specific_config,
          taskmodels_config=taskmodels_config,
          task_run_config=task_run_config,
          metrics_aggregator=metric_aggregator,
      )
  
imports: import warnings
  from dataclasses import dataclass
  from typing import Dict, List, Optional
  import jiant.proj.main.components.task_sampler
  import jiant.shared.caching
  from jiant.tasks.core import Task
  from jiant.tasks.retrieval import create_task_from_config_path
  import jiant.utils.python.io
  from jiant.utils.python.datastructures import ExtendedDataClassMixin
label_desc: Use image node_id 5 for calling a global function with example usage: create_jiant_task_container(task_config_path_dict, task_cache_config_dict, sampler_config, global_train_config, task_specific_configs_dict, metric_aggregator_config, taskmodels_config, task_run_config, verbose) and returns: JiantTaskContainer