      elif norm == 'batchnorm':
          var_x = BatchNormalization()(inp)
      elif norm == 'groupnorm':
          var_x = GroupNormalization(group=16)(inp)
      elif norm == 'instancenorm':
          var_x = InstanceNormalization()(inp)
      elif norm == 'hybrid':
          if group % 2 == 1:
              raise ValueError("Output channels must be an even number for hybrid norm, "
                               "received {}.".format(group))
 
</s> remove     elif norm == 'hybrid':
 </s> add     elif norm == "hybrid": </s> remove     elif norm == 'groupnorm':
 </s> add     elif norm == "groupnorm": </s> remove     elif norm == 'batchnorm':
 </s> add     elif norm == "batchnorm": </s> remove     if norm == 'layernorm':
 </s> add     if norm == "layernorm": </s> remove def normalization(inp, norm='none', group='16'):
 </s> add def normalization(inp, norm="none", group="16"):
    elif norm == "instancenorm":
      elif norm == 'batchnorm':
          var_x = BatchNormalization()(inp)
      elif norm == 'groupnorm':
          var_x = GroupNormalization(group=16)(inp)
    elif norm == "instancenorm":
          var_x = InstanceNormalization()(inp)
      elif norm == 'hybrid':
          if group % 2 == 1:
              raise ValueError("Output channels must be an even number for hybrid norm, "
                               "received {}.".format(group))
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