          self.gamma_regularizer = regularizers.get(gamma_regularizer)
          self.beta_regularizer = regularizers.get(beta_regularizer)
          self.epsilon = epsilon
          self.group = group
          self.data_format = K.normalize_data_format(data_format)
  
          self.supports_masking = True
  
      def build(self, input_shape):
          """Creates the layer weights.
 
</s> remove def test_layer_normalization(center, scale):  # pylint:disable=unused-argument
 </s> add def test_adain_normalization(center, scale):
    """ Basic test for Ada Instance Normalization. """
    norm = normalization.AdaInstanceNormalization(center=center, scale=scale)
    shapes = [(4, 8, 8, 1280), (4, 1, 1, 1280), (4, 1, 1, 1280)]
    norm.build(shapes)
    expected_output_shape = norm.compute_output_shape(shapes)
    inputs = [layers.Input(shape=shapes[0][1:]),
              layers.Input(shape=shapes[1][1:]),
              layers.Input(shape=shapes[2][1:])]
    model = models.Model(inputs, norm(inputs))
    data = [10 * np.random.random(shape) for shape in shapes]

    actual_output = model.predict(data)
    actual_output_shape = actual_output.shape

    for expected_dim, actual_dim in zip(expected_output_shape,
                                        actual_output_shape):
        if expected_dim is not None:
            assert expected_dim == actual_dim


@pytest.mark.parametrize(_PARAMS, _VALUES, ids=_IDS)
def test_layer_normalization(center, scale): </s> remove from .normalization_common import *
 </s> add from .normalization_common import AdaInstanceNormalization
from .normalization_common import GroupNormalization
from .normalization_common import InstanceNormalization
 </s> remove from keras import regularizers
 </s> add import numpy as np
        self.data_format = normalize_data_format(data_format)
          self.gamma_regularizer = regularizers.get(gamma_regularizer)
          self.beta_regularizer = regularizers.get(beta_regularizer)
          self.epsilon = epsilon
          self.group = group
        self.data_format = normalize_data_format(data_format)
  
          self.supports_masking = True
  
      def build(self, input_shape):
          """Creates the layer weights.
keep keep keep keep replace keep keep keep keep keep
 Unit tests - lib.model.normalization
  - __init__.py - Explicit imports (to fix pytests)
  - Add tests for all existing normalization layers
  - fix GroupNormalization for tensorflow backend 
lib/model/normalization/normalization_common.py