code: def _clip_iqa_get_anchor_vectors(
      model_name_or_path: str,
      model: "_CLIPModel",
      processor: "_CLIPProcessor",
      prompts_list: List[str],
      device: Union[str, torch.device],
  ) -> Tensor:
      """Calculates the anchor vectors for the CLIP IQA metric.
  
      Args:
          model_name_or_path: string indicating the version of the CLIP model to use.
          model: The CLIP model
          processor: The CLIP processor
          prompts_list: A list of prompts
          device: The device to use for the calculation
  
      """
      if model_name_or_path == "clip_iqa":
          text_processed = processor(text=prompts_list)
          anchors_text = torch.zeros(
              len(prompts_list),
              processor.tokenizer.model_max_length,
              dtype=torch.long,
              device=device,
          )
          for i, tp in enumerate(text_processed["input_ids"]):
              anchors_text[i, : len(tp)] = torch.tensor(
                  tp, dtype=torch.long, device=device
              )
  
          anchors = model.encode_text(anchors_text).float()
      else:
          text_processed = processor(
              text=prompts_list, return_tensors="pt", padding=True
          )
          anchors = model.get_text_features(
              text_processed["input_ids"].to(device),
              text_processed["attention_mask"].to(device),
          )
      return anchors / anchors.norm(p=2, dim=-1, keepdim=True)
  
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 3 for calling a global function with example usage: _clip_iqa_get_anchor_vectors(model_name_or_path, model, processor, prompts_list, device) and returns: unknown