class_docstr: Handles the `task name -> model class` and `architecture -> configuration` mappings.
docstr: Returns the list of supported architectures by the exporter for a given task.
docstr_tok: ["Returns","the","list","of","supported","architectures","by","the","exporter","for","a","given","task","."]
code: def get_supported_model_type_for_task(
      task: str, exporter: str
  ) -> List[str]:
      """
      Returns the list of supported architectures by the exporter for a given task.
      """
      return [
          model_type.replace("-", "_")
          for model_type in TasksManager._SUPPORTED_MODEL_TYPE
          if task
          in TasksManager._SUPPORTED_MODEL_TYPE[model_type][exporter]
      ]
  
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 3 for calling the TasksManager obj's underlying member method code with example usage: obj.get_supported_model_type_for_task(task, exporter) and returns: unknown