class_docstr: Handles the `task name -> model class` and `architecture -> configuration` mappings.
docstr: Retrieves all the possible tasks.
  
  Returns:
      `List`: all the possible tasks.
docstr_tok: ["Retrieves","all","the","possible","tasks",".","Returns",":","`","List","`",":","all","the","possible","tasks","."]
code: def get_all_tasks():
      """
      Retrieves all the possible tasks.
  
      Returns:
          `List`: all the possible tasks.
      """
      tasks = []
      if is_torch_available():
          mapping = TasksManager._LIBRARY_TO_TASKS_TO_MODEL_LOADER_MAP
      else:
          mapping = (
              TasksManager._LIBRARY_TO_TF_TASKS_TO_MODEL_LOADER_MAP
          )
  
      tasks = []
      for d in mapping.values():
          tasks += list(d.keys())
  
      tasks = list(set(tasks))
  
      return tasks
  
imports: import importlib
  import inspect
  import itertools
  import json
  import os
  from functools import partial
  from pathlib import Path
  from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union
  import huggingface_hub
  from packaging import version
  from requests.exceptions import ConnectionError
  from transformers import AutoConfig, PretrainedConfig, is_tf_available, is_torch_available
  from transformers.utils import SAFE_WEIGHTS_NAME, TF2_WEIGHTS_NAME, WEIGHTS_NAME, logging
  from ..utils import CONFIG_NAME
  from ..utils.import_utils import is_onnx_available
label_desc: Use image node_id 15 for calling the TasksManager obj's underlying member method code with example usage: obj.get_all_tasks() and returns: tasks