  from keras.utils.generic_utils import get_custom_objects
  
  
  def icnr_keras(shape, dtype=None):
      """
      Custom initializer for subpix upscaling
 
</s> remove from keras.initializers import he_uniform, Constant, VarianceScaling
from .initializers import ICNR
 </s> add from keras.initializers import he_uniform, VarianceScaling
from .initializers import ICNR, ConvolutionAware </s> remove from keras.layers import Conv2D, Input
 </s> add from keras.layers import Input </s> remove from keras.layers import Conv2D, Dense, Flatten, Input, Reshape
 </s> add from keras.layers import Dense, Flatten, Input, Reshape
logger = logging.getLogger(__name__)  # pylint: disable=invalid-name

  from keras.utils.generic_utils import get_custom_objects
  
logger = logging.getLogger(__name__)  # pylint: disable=invalid-name

  
  def icnr_keras(shape, dtype=None):
      """
      Custom initializer for subpix upscaling
keep add 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/initializers.py