class_docstr: Test clip iqa metric.
class_docstr_tok: ["Test","clip","iqa","metric","."]
docstr: Test functional implementation of metric.
docstr_tok: ["Test","functional","implementation","of","metric","."]
code: def test_clip_iqa_functional(self, shapes):
      """Test functional implementation of metric."""
      img = torch.rand(shapes)
      self.run_functional_metric_test(
          preds=img,
          target=img,
          metric_functional=_clip_iqa_tester,
          reference_metric=_reference,
      )
  
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 2 for calling the TestCLIPIQA obj's underlying member method code with example usage: obj.test_clip_iqa_functional(shapes) without return types