  from keras import backend as K
  from keras.utils import get_custom_objects
  
  
  class InstanceNormalization(Layer):
      """Instance normalization layer (Lei Ba et al, 2016, Ulyanov et al., 2016).
  
      Normalize the activations of the previous layer at each step, i.e. applies a transformation
 
</s> remove from keras import regularizers
 </s> add import numpy as np </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 .normalization_plaid import *
 </s> add     from .normalization_plaid import LayerNormalization, RMSNormalization </s> remove     from .normalization_tf import *
 </s> add     from .normalization_tf import LayerNormalization, RMSNormalization
from lib.utils import get_backend


if get_backend() == "amd":
    from keras.backend import normalize_data_format  # pylint:disable=ungrouped-imports
else:
    from tensorflow.python.keras.utils.conv_utils import normalize_data_format

  from keras import backend as K
  from keras.utils import get_custom_objects
  
from lib.utils import get_backend


if get_backend() == "amd":
    from keras.backend import normalize_data_format  # pylint:disable=ungrouped-imports
else:
    from tensorflow.python.keras.utils.conv_utils import normalize_data_format

  
  class InstanceNormalization(Layer):
      """Instance normalization layer (Lei Ba et al, 2016, Ulyanov et al., 2016).
  
      Normalize the activations of the previous layer at each step, i.e. applies a transformation
keep keep add 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