class_docstr: Handles the `task name -> model class` and `architecture -> configuration` mappings.
code: def _infer_task_from_model_name_or_path(
      cls,
      model_name_or_path: str,
      subfolder: str = "",
      revision: Optional[str] = None,
  ) -> str:
      inferred_task_name = None
      is_local = os.path.isdir(
          os.path.join(model_name_or_path, subfolder)
      )
  
      if is_local:
          # TODO: maybe implement that.
          raise RuntimeError(
              "Cannot infer the task from a local directory yet, please specify the task manually."
          )
      else:
          if subfolder != "":
              raise RuntimeError(
                  "Cannot infer the task from a model repo with a subfolder yet, please specify the task manually."
              )
          model_info = huggingface_hub.model_info(
              model_name_or_path, revision=revision
          )
          if getattr(model_info, "library_name", None) == "diffusers":
              class_name = model_info.config["diffusers"]["class_name"]
              inferred_task_name = (
                  "stable-diffusion-xl"
                  if "StableDiffusionXL" in class_name
                  else "stable-diffusion"
              )
          elif getattr(model_info, "library_name", None) == "timm":
              inferred_task_name = "image-classification"
          else:
              pipeline_tag = getattr(model_info, "pipeline_tag", None)
              # The Hub task "conversational" is not a supported task per se, just an alias that may map to
              # text-generaton or text2text-generation.
              # The Hub task "object-detection" is not a supported task per se, as in Transformers this may map to either
              # zero-shot-object-detection or object-detection.
              if pipeline_tag is not None and pipeline_tag not in [
                  "conversational",
                  "object-detection",
              ]:
                  inferred_task_name = TasksManager.map_from_synonym(
                      model_info.pipeline_tag
                  )
              else:
                  transformers_info = model_info.transformersInfo
                  if (
                      transformers_info is not None
                      and transformers_info.get("pipeline_tag")
                      is not None
                  ):
                      inferred_task_name = (
                          TasksManager.map_from_synonym(
                              transformers_info["pipeline_tag"]
                          )
                      )
                  else:
                      # transformersInfo does not always have a pipeline_tag attribute
                      class_name_prefix = ""
                      if is_torch_available():
                          tasks_to_automodels = TasksManager._LIBRARY_TO_TASKS_TO_MODEL_LOADER_MAP[
                              model_info.library_name
                          ]
                      else:
                          tasks_to_automodels = TasksManager._LIBRARY_TO_TF_TASKS_TO_MODEL_LOADER_MAP[
                              model_info.library_name
                          ]
                          class_name_prefix = "TF"
  
                      auto_model_class_name = transformers_info[
                          "auto_model"
                      ]
                      if not auto_model_class_name.startswith("TF"):
                          auto_model_class_name = f"{class_name_prefix}{auto_model_class_name}"
                      for (
                          task_name,
                          class_name_for_task,
                      ) in tasks_to_automodels.items():
                          if (
                              class_name_for_task
                              == auto_model_class_name
                          ):
                              inferred_task_name = task_name
                              break
  
      if inferred_task_name is None:
          raise KeyError(
              f"Could not find the proper task name for {auto_model_class_name}."
          )
      return inferred_task_name
  
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 11 for calling the TasksManager obj's underlying member method code with example usage: obj._infer_task_from_model_name_or_path(cls, model_name_or_path, subfolder, revision) and returns: inferred_task_name