code: def test_bachnorm_ignore_masked_values(self, run_eagerly, mask_value):
      padded_data = np.array(
          [
              [
                  [1, 5],
                  [2, 5],
                  [mask_value, mask_value],
                  [mask_value, mask_value],
              ]
              for _ in range(10)
          ],
          dtype="float32",
      )
  
      inputs = layers.Input((None, 2))
      masked = layers.Masking(mask_value=mask_value)(inputs)
      normed = layers.BatchNormalization(momentum=0.0)(masked)
      model = Model(inputs, normed)
      loss = MeanSquaredError()
      model.compile(
          "rmsprop",
          loss=loss,
          run_eagerly=run_eagerly,
      )
      model.fit(x=padded_data, y=padded_data, batch_size=10, epochs=5)
      self.assertAllClose(
          model.layers[2].moving_mean.numpy(), [1.5, 5.0]
      )
      self.assertAllClose(
          model.layers[2].moving_variance.numpy(), [0.25, 0.0]
      )
  
file_path: keras/keras/layers/normalization/batch_normalization_test.py
imports: import numpy
  import pytest
  from absl.testing import parameterized
  from keras import backend
  from keras import layers
  from keras import testing
  from keras.losses import MeanSquaredError
  from keras.models import Model
label_desc: Use image node_id 4 for calling the BatchNormalizationTest obj's underlying member method code with example usage: obj.test_bachnorm_ignore_masked_values(run_eagerly, mask_value) without return types