code: def _clip_iqa_compute(
      img_features: Tensor,
      anchors: Tensor,
      prompts_names: List[str],
      format_as_dict: bool = True,
  ) -> Union[Tensor, Dict[str, Tensor]]:
      """Final computation of CLIP IQA."""
      logits_per_image = 100 * img_features @ anchors.t()
      probs = logits_per_image.reshape(
          logits_per_image.shape[0], -1, 2
      ).softmax(-1)[:, :, 0]
      if len(prompts_names) == 1:
          return probs.squeeze()
      if format_as_dict:
          return {p: probs[:, i] for i, p in enumerate(prompts_names)}
      return probs
  
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 5 for calling a global function with example usage: _clip_iqa_compute(img_features, anchors, prompts_names, format_as_dict) and returns: probs, probs, unknown