  from .instance_normalization import InstanceNormalization
  
  netGAH5 = 'netGA_GAN128.h5'
  netGBH5 = 'netGB_GAN128.h5'
  netDAH5 = 'netDA_GAN128.h5'
  netDBH5 = 'netDB_GAN128.h5'
  
  def __conv_init(a):
 
</s> remove         self.netGA.save_weights(str(self.model_dir / netGAH5))
        self.netGB.save_weights(str(self.model_dir / netGBH5))
 </s> add         if self.gpus > 1:
            self.netGA_sm.save_weights(str(self.model_dir / netGAH5))
            self.netGB_sm.save_weights(str(self.model_dir / netGBH5))
        else:
            self.netGA.save_weights(str(self.model_dir / netGAH5))
            self.netGB.save_weights(str(self.model_dir / netGBH5))

from keras.utils import multi_gpu_model


  from .instance_normalization import InstanceNormalization
  
from keras.utils import multi_gpu_model

  netGAH5 = 'netGA_GAN128.h5'
  netGBH5 = 'netGB_GAN128.h5'
  netDAH5 = 'netDA_GAN128.h5'
  netDBH5 = 'netDB_GAN128.h5'
  
  def __conv_init(a):
keep add keep keep keep keep keep keep
 Fix gan128 (#288)

* Complete parity fix for GAN128.

This brings GAN128 to parity with GAN in terms of multi GPU support.

Unfortunately, it fails to run due to a naming error.

('The name "model_4" is used 2 times in the model. All layer names should be unique. Layer names: ', ['input_6', 'lambda_1', 'lambda_2', 'model_6', 'model_4', 'model_4'])

* Fix for GAN128 until #287 can be resolved

Issue #287 details why GAN128 cannot be fixed until Keras is fixed upstream.

* Changes to PR as per Clorr

Made changes to error handling to split into separate PR as requested by Clorr. 
plugins/Model_GAN128/Model.py