code: def _get_clip_model_and_processor(
      model_name_or_path: Literal[
          "openai/clip-vit-base-patch16",
          "openai/clip-vit-base-patch32",
          "openai/clip-vit-large-patch14-336",
          "openai/clip-vit-large-patch14",
      ] = "openai/clip-vit-large-patch14",
  ) -> Tuple[_CLIPModel, _CLIPProcessor]:
      if _TRANSFORMERS_GREATER_EQUAL_4_10:
          from transformers import CLIPModel as _CLIPModel
          from transformers import CLIPProcessor as _CLIPProcessor
  
          model = _CLIPModel.from_pretrained(model_name_or_path)
          processor = _CLIPProcessor.from_pretrained(model_name_or_path)
          return model, processor
  
      raise ModuleNotFoundError(
          "`clip_score` metric requires `transformers` package be installed."
          " Either install with `pip install transformers>=4.10.0` or `pip install torchmetrics[multimodal]`."
      )
  
file_path: torchmetrics/src/torchmetrics/functional/multimodal/clip_score.py
imports: from typing import TYPE_CHECKING, List, Tuple, Union
  import torch
  from torch import Tensor
  from typing_extensions import Literal
  from torchmetrics.utilities import rank_zero_warn
  from torchmetrics.utilities.checks import _SKIP_SLOW_DOCTEST, _try_proceed_with_timeout
  from torchmetrics.utilities.imports import _TRANSFORMERS_GREATER_EQUAL_4_10
label_desc: Use image node_id 2 for calling a global function with example usage: _get_clip_model_and_processor(model_name_or_path) and returns: model,  processor