  GAN22_REGULARIZER = 1e-4
  
  
  # Gan Blocks:
  def normalization(inp, norm='none', group='16'):
      """ GAN Normalization """
      if norm == 'layernorm':
          var_x = GroupNormalization(group=group)(inp)
      elif norm == 'batchnorm':
          var_x = BatchNormalization()(inp)
 
</s> remove     if norm == 'layernorm':
 </s> add     if norm == "layernorm": </s> remove     elif norm == 'batchnorm':
 </s> add     elif norm == "batchnorm": </s> remove     elif norm == 'groupnorm':
 </s> add     elif norm == "groupnorm": </s> remove     elif norm == 'instancenorm':
 </s> add     elif norm == "instancenorm": </s> remove     elif norm == 'hybrid':
 </s> add     elif norm == "hybrid":
def normalization(inp, norm="none", group="16"):
  GAN22_REGULARIZER = 1e-4
  
  
  # Gan Blocks:
def normalization(inp, norm="none", group="16"):
      """ GAN Normalization """
      if norm == 'layernorm':
          var_x = GroupNormalization(group=group)(inp)
      elif norm == 'batchnorm':
          var_x = BatchNormalization()(inp)
keep keep keep keep 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