code: def test_other_models(model):
      """Test that the function works with other models."""
      img = Image.open(_SAMPLE_IMAGE)
      img = PILToTensor()(img)
      img = img.float()[None]
  
      reference = piq.CLIPIQA(data_range=255)
      reference_score = reference(img)
  
      result = clip_image_quality_assessment(
          img, data_range=255, model_name_or_path=model
      )
      # allow large difference between scores due to different models, but still in the same ballpark
      assert reference_score - 0.2 < result < reference_score + 0.2
  
imports: import os
  from functools import partial
  import matplotlib
  import matplotlib.pyplot
  import piq
  import pytest
  import torch
  from PIL import Image
  from torch import Tensor
  from torchmetrics.functional.multimodal.clip_iqa import clip_image_quality_assessment
  from torchmetrics.multimodal.clip_iqa import CLIPImageQualityAssessment
  from torchmetrics.utilities.imports import _PIQ_GREATER_EQUAL_0_8, _TRANSFORMERS_GREATER_EQUAL_4_10
  from torchvision.transforms import PILToTensor
  from unittests.helpers.testers import MetricTester
  from unittests.image import _SAMPLE_IMAGE
  from unittests.text.helpers import skip_on_connection_issues
label_desc: Use image node_id 5 for calling a global function with example usage: test_other_models(model) without return types