code: def _get_clip_iqa_model_and_processor(
      model_name_or_path: Literal[
          "clip_iqa",
          "openai/clip-vit-base-patch16",
          "openai/clip-vit-base-patch32",
          "openai/clip-vit-large-patch14-336",
          "openai/clip-vit-large-patch14",
      ]
  ) -> Tuple["_CLIPModel", "_CLIPProcessor"]:
      """Extract the CLIP model and processor from the model name or path."""
      from transformers import CLIPProcessor as _CLIPProcessor
  
      if model_name_or_path == "clip_iqa":
          if not _PIQ_GREATER_EQUAL_0_8:
              raise ValueError(
                  "For metric `clip_iqa` to work with argument `model_name_or_path` set to default value `'clip_iqa'`"
                  ", package `piq` version v0.8.0 or later must be installed. Either install with `pip install piq` or"
                  "`pip install torchmetrics[multimodal]`"
              )
  
          import piq
  
          model = piq.clip_iqa.clip.load().eval()
          # any model checkpoint can be used here because the tokenizer is the same for all
          processor = _CLIPProcessor.from_pretrained(
              "openai/clip-vit-base-patch16"
          )
          return model, processor
      return _get_clip_model_and_processor(model_name_or_path)
  
file_path: torchmetrics/src/torchmetrics/functional/multimodal/clip_iqa.py
imports: from typing import TYPE_CHECKING, Dict, List, Literal, Tuple, Union
  import torch
  from torch import Tensor
  from torchmetrics.functional.multimodal.clip_score import _get_clip_model_and_processor
  from torchmetrics.utilities.checks import _SKIP_SLOW_DOCTEST, _try_proceed_with_timeout
  from torchmetrics.utilities.imports import _PIQ_GREATER_EQUAL_0_8, _TRANSFORMERS_GREATER_EQUAL_4_10
label_desc: Use image node_id 1 for calling a global function with example usage: _get_clip_iqa_model_and_processor(model_name_or_path) and returns: _get_clip_model_and_processor, model,  processor