class_docstr: Handles the `task name -> model class` and `architecture -> configuration` mappings.
docstr: Updates the model for export. This function is suitable to make required changes to the models from different
  libraries to follow transformers style.
  
  Args:
      model_name_or_path (`Union[str, Path]`):
          Can be either the model id of a model repo on the Hugging Face Hub, or a path to a local directory
          containing a model.
      model (`Union[PreTrainedModel, TFPreTrainedModel]`):
          The instance of the model.
      subfolder (`str`, 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]`, *optional*, defaults to `None`):
          Revision is the specific model version to use. It can be a branch name, a tag name, or a commit id.
      cache_dir (`Optional[str]`, *optional*):
          Path to a directory in which a downloaded pretrained model weights have been cached if the standard cache should not be used.
      library_name (`Optional[str]`, *optional*)::
           The library name of the model.
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code: def standardize_model_attributes(
      cls,
      model_name_or_path: Union[str, Path],
      model: Union["PreTrainedModel", "TFPreTrainedModel"],
      subfolder: str = "",
      revision: Optional[str] = None,
      cache_dir: str = huggingface_hub.constants.HUGGINGFACE_HUB_CACHE,
      library_name: str = None,
  ):
      """
      Updates the model for export. This function is suitable to make required changes to the models from different
      libraries to follow transformers style.
  
      Args:
          model_name_or_path (`Union[str, Path]`):
              Can be either the model id of a model repo on the Hugging Face Hub, or a path to a local directory
              containing a model.
          model (`Union[PreTrainedModel, TFPreTrainedModel]`):
              The instance of the model.
          subfolder (`str`, 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]`, *optional*, defaults to `None`):
              Revision is the specific model version to use. It can be a branch name, a tag name, or a commit id.
          cache_dir (`Optional[str]`, *optional*):
              Path to a directory in which a downloaded pretrained model weights have been cached if the standard cache should not be used.
          library_name (`Optional[str]`, *optional*)::
               The library name of the model.
      """
      library_name = TasksManager.infer_library_from_model(
          model_name_or_path,
          subfolder,
          revision,
          cache_dir,
          library_name,
      )
  
      full_model_path = Path(model_name_or_path) / subfolder
      is_local = full_model_path.is_dir()
  
      if library_name == "timm":
          # Retrieve model config
          config_path = full_model_path / "config.json"
  
          if not is_local:
              config_path = huggingface_hub.hf_hub_download(
                  model_name_or_path,
                  "config.json",
                  subfolder=subfolder,
                  revision=revision,
              )
  
          model_config = PretrainedConfig.from_json_file(config_path)
  
          if hasattr(model_config, "pretrained_cfg"):
              model_config.pretrained_cfg = PretrainedConfig.from_dict(
                  model_config.pretrained_cfg
              )
  
          # Set config as in transformers
          setattr(model, "config", model_config)
  
          # Update model_type for model
          with open(config_path) as fp:
              model_type = json.load(fp)["architecture"]
  
          setattr(model.config, "model_type", model_type)
      elif library_name == "sentence_transformers":
          if "Transformer" in model[0].__class__.__name__:
              model.config = model[0].auto_model.config
              model.config.model_type = (
                  "sentence-transformers-transformer"
              )
          elif "CLIP" in model[0].__class__.__name__:
              model.config = model[0].model.config
              model.config.model_type = "sentence-transformers-clip"
          else:
              raise ValueError(
                  f"The export of a sentence-transformers model with the first module being {model[0].__class__.__name__} is currently not supported in Optimum. Please open an issue or submit a PR to add the support."
              )
  
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 14 for calling the TasksManager obj's underlying member method code with example usage: obj.standardize_model_attributes(cls, model_name_or_path, model, subfolder, revision, cache_dir, library_name) without return types