docstr: Check class distribution with uneven samples.
docstr_tok: ["Check","class","distribution","with","uneven","samples","."]
code: def test_uneven_samples_class_distribution(self):
      """Check class distribution with uneven samples."""
      train_samples = 103
      test_samples = 52
      (_, y_train), (_, y_test) = test_utils.get_test_data(
          train_samples,
          test_samples,
          self.input_shape,
          self.num_classes,
      )
      _, counts_train = np.unique(y_train, return_counts=True)
      _, counts_test = np.unique(y_test, return_counts=True)
  
      self.assertTrue(np.max(counts_train) - np.min(counts_train) <= 1)
      self.assertTrue(np.max(counts_test) - np.min(counts_test) <= 1)
  
imports: import numpy
  from absl.testing import parameterized
  from keras.testing import test_case
  from keras.testing import test_utils
label_desc: Use image node_id 3 for calling the ClassDistributionTests obj's underlying member method code with example usage: obj.test_uneven_samples_class_distribution() without return types