                            Permute, Reshape, SeparableConv2D, Softmax, UpSampling2D)
  from keras.layers.advanced_activations import LeakyReLU
  from keras.layers.convolutional import Conv2D
  from keras.layers.core import Activation
  from keras.initializers import he_uniform, Constant, VarianceScaling
  from .initializers import ICNR
  from .layers import PixelShuffler, SubPixelUpscaling, ReflectionPadding2D, Scale
  from .normalization import GroupNormalization, InstanceNormalization
  
  logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
  
 
</s> remove from keras.layers import Conv2D, Dense, Flatten, Input, Reshape, SpatialDropout2D
 </s> add from keras.layers import Dense, Flatten, Input, Reshape, SpatialDropout2D </s> remove from keras.layers import Conv2D, Dense, Flatten, Input, Reshape
 </s> add from keras.layers import Dense, Flatten, Input, Reshape </s> remove from keras.layers import Conv2D, Input
 </s> add from keras.layers import Input
from keras.initializers import he_uniform, VarianceScaling
from .initializers import ICNR, ConvolutionAware
                            Permute, Reshape, SeparableConv2D, Softmax, UpSampling2D)
  from keras.layers.advanced_activations import LeakyReLU
  from keras.layers.convolutional import Conv2D
  from keras.layers.core import Activation
from keras.initializers import he_uniform, VarianceScaling
from .initializers import ICNR, ConvolutionAware
from keras.initializers import he_uniform, VarianceScaling
from .initializers import ICNR, ConvolutionAware
  from .layers import PixelShuffler, SubPixelUpscaling, ReflectionPadding2D, Scale
  from .normalization import GroupNormalization, InstanceNormalization
  
  logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
  
keep keep keep keep replace replace keep keep keep keep keep
 Add Convolutional Aware Initialization (#795)

* Training: Add Convolutional Aware Initialization config option

* Centralize Conv2D layer for handling initializer

* Add 'is-output' to NNMeta to indicate that network is an output to the Model 
lib/model/nn_blocks.py