code: def test_get_label_quality_scores_sizes():
      # checks inputs of different sizes
      labels, pred_probs = np.random.randint(
          0, 2, (2, 9, 7)
      ), np.random.random((2, 2, 9, 7))
      image_scores_softmin, pixel_scores = get_label_quality_scores(
          labels, pred_probs, method="softmin"
      )
  
      labels, pred_probs = np.random.randint(
          0, 2, (2, 13, 47)
      ), np.random.random((2, 2, 13, 47))
      image_scores_softmin, pixel_scores = get_label_quality_scores(
          labels, pred_probs, method="softmin"
      )
  
      for _ in range(5):
          h, w = np.random.randint(1, 100, 2)
          labels, pred_probs = np.random.randint(
              0, 2, (2, h, w)
          ), np.random.random((2, 2, h, w))
          image_scores_softmin, pixel_scores = get_label_quality_scores(
              labels, pred_probs, method="softmin"
          )
  
imports: import numpy
  import random
  import pytest
  from unittest import mock
  import matplotlib.pyplot
  from pathlib import Path
  from cleanlab.internal.multilabel_scorer import softmin
  from cleanlab.internal.segmentation_utils import _check_input, _get_valid_optional_params, _get_summary_optional_params
  from cleanlab.segmentation.filter import find_label_issues
  from cleanlab.segmentation.rank import get_label_quality_scores, issues_from_scores, _get_label_quality_per_image
  from cleanlab.segmentation.summary import display_issues, common_label_issues, filter_by_class, _generate_colormap
label_desc: Use image node_id 8 for calling a global function with example usage: test_get_label_quality_scores_sizes() without return types