class_docstr: Handles the `task name -> model class` and `architecture -> configuration` mappings.
docstr: Infers the task from the model repo.
  
  Args:
      model (`str`):
          The model to infer the task from. This can either be the name of a repo on the HuggingFace Hub, an
          instance of a model, or a model class.
      subfolder (`str`, *optional*, defaults to `""`):
          In case the model files are located inside a subfolder of the model directory / repo on the Hugging
          Face Hub, you can specify the subfolder name here.
      revision (`Optional[str]`,  defaults to `None`):
          Revision is the specific model version to use. It can be a branch name, a tag name, or a commit id.
  Returns:
      `str`: The task name automatically detected from the model repo.
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code: def infer_task_from_model(
      cls,
      model: Union[str, "PreTrainedModel", "TFPreTrainedModel", Type],
      subfolder: str = "",
      revision: Optional[str] = None,
  ) -> str:
      """
      Infers the task from the model repo.
  
      Args:
          model (`str`):
              The model to infer the task from. This can either be the name of a repo on the HuggingFace Hub, an
              instance of a model, or a model class.
          subfolder (`str`, *optional*, defaults to `""`):
              In case the model files are located inside a subfolder of the model directory / repo on the Hugging
              Face Hub, you can specify the subfolder name here.
          revision (`Optional[str]`,  defaults to `None`):
              Revision is the specific model version to use. It can be a branch name, a tag name, or a commit id.
      Returns:
          `str`: The task name automatically detected from the model repo.
      """
      is_torch_pretrained_model = is_torch_available() and isinstance(
          model, PreTrainedModel
      )
      is_tf_pretrained_model = is_tf_available() and isinstance(
          model, TFPreTrainedModel
      )
      task = None
      if isinstance(model, str):
          task = cls._infer_task_from_model_name_or_path(
              model, subfolder=subfolder, revision=revision
          )
      elif is_torch_pretrained_model or is_tf_pretrained_model:
          task = cls._infer_task_from_model_or_model_class(model=model)
      elif inspect.isclass(model):
          task = cls._infer_task_from_model_or_model_class(
              model_class=model
          )
  
      if task is None:
          raise ValueError(f"Could not infer the task from {model}.")
  
      return task
  
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 12 for calling the TasksManager obj's underlying member method code with example usage: obj.infer_task_from_model(cls, model, subfolder, revision) and returns: task