          dense_shape = self.input_shape[0] // 16
          input_ = Input(shape=self.input_shape)
  
          var_x = input_
          var_x = Conv2DBlock(encoder_complexity, use_instance_norm=True, **kwargs)(var_x)
          var_x = Conv2DBlock(encoder_complexity * 2, use_instance_norm=True, **kwargs)(var_x)
          var_x = Conv2DBlock(encoder_complexity * 4, **kwargs)(var_x)
          var_x = Conv2DBlock(encoder_complexity * 6, **kwargs)(var_x)
          var_x = Conv2DBlock(encoder_complexity * 8, **kwargs)(var_x)
          var_x = Dense(self.encoder_dim,
                        kernel_initializer=self.kernel_initializer)(Flatten()(var_x))
          var_x = Dense(dense_shape * dense_shape * dense_dim,
                        kernel_initializer=self.kernel_initializer)(var_x)
          var_x = Reshape((dense_shape, dense_shape, dense_dim))(var_x)
 
</s> remove         var_x = UpscaleBlock(512, **kwargs)(var_x)
 </s> add         var_x = UpscaleBlock(512, activation="leakyrelu", **kwargs)(var_x) </s> remove         var_x = Conv2DBlock(512, **kwargs)(var_x)
 </s> add         var_x = Conv2DBlock(512, activation="leakyrelu", **kwargs)(var_x) </s> remove             var_x = Conv2DBlock(encoder_complexity * 2**idx)(var_x)
            var_x = ResidualBlock(encoder_complexity * 2**idx, use_bias=True)(var_x)
            var_x = ResidualBlock(encoder_complexity * 2**idx, use_bias=True)(var_x)
 </s> add             var_x = Conv2DBlock(encoder_complexity * 2**idx, activation="leakyrelu")(var_x)
            var_x = ResidualBlock(encoder_complexity * 2**idx,
                                  use_bias=True,
                                  activation="leakyrelu")(var_x)
            var_x = ResidualBlock(encoder_complexity * 2**idx,
                                  use_bias=True,
                                  activation="leakyrelu")(var_x) </s> remove         var_x = Conv2DBlock(encoder_complexity * 2**(idx + 1))(var_x)
 </s> add         var_x = Conv2DBlock(encoder_complexity * 2**(idx + 1), activation="leakyrelu")(var_x) </s> remove             var_x = UpscaleBlock(decoder_complexity, **kwargs)(var_x)
            var_x = UpscaleBlock(decoder_complexity // 2, **kwargs)(var_x)
            var_x = UpscaleBlock(decoder_complexity // 4, **kwargs)(var_x)
            var_x = UpscaleBlock(decoder_complexity // 8, **kwargs)(var_x)
 </s> add             var_x = UpscaleBlock(decoder_complexity, activation="leakyrelu", **kwargs)(var_x)
            var_x = UpscaleBlock(decoder_complexity // 2, activation="leakyrelu", **kwargs)(var_x)
            var_x = UpscaleBlock(decoder_complexity // 4, activation="leakyrelu", **kwargs)(var_x)
            var_x = UpscaleBlock(decoder_complexity // 8, activation="leakyrelu", **kwargs)(var_x)
        var_x = Conv2DBlock(encoder_complexity,
                            normalization="instance",
                            activation="leakyrelu",
                            **kwargs)(var_x)
        var_x = Conv2DBlock(encoder_complexity * 2,
                            normalization="instance",
                            activation="leakyrelu",
                            **kwargs)(var_x)
        var_x = Conv2DBlock(encoder_complexity * 4, **kwargs, activation="leakyrelu")(var_x)
        var_x = Conv2DBlock(encoder_complexity * 6, **kwargs, activation="leakyrelu")(var_x)
        var_x = Conv2DBlock(encoder_complexity * 8, **kwargs, activation="leakyrelu")(var_x)
          dense_shape = self.input_shape[0] // 16
          input_ = Input(shape=self.input_shape)
  
          var_x = input_
        var_x = Conv2DBlock(encoder_complexity,
                            normalization="instance",
                            activation="leakyrelu",
                            **kwargs)(var_x)
        var_x = Conv2DBlock(encoder_complexity * 2,
                            normalization="instance",
                            activation="leakyrelu",
                            **kwargs)(var_x)
        var_x = Conv2DBlock(encoder_complexity * 4, **kwargs, activation="leakyrelu")(var_x)
        var_x = Conv2DBlock(encoder_complexity * 6, **kwargs, activation="leakyrelu")(var_x)
        var_x = Conv2DBlock(encoder_complexity * 8, **kwargs, activation="leakyrelu")(var_x)
        var_x = Conv2DBlock(encoder_complexity,
                            normalization="instance",
                            activation="leakyrelu",
                            **kwargs)(var_x)
        var_x = Conv2DBlock(encoder_complexity * 2,
                            normalization="instance",
                            activation="leakyrelu",
                            **kwargs)(var_x)
        var_x = Conv2DBlock(encoder_complexity * 4, **kwargs, activation="leakyrelu")(var_x)
        var_x = Conv2DBlock(encoder_complexity * 6, **kwargs, activation="leakyrelu")(var_x)
        var_x = Conv2DBlock(encoder_complexity * 8, **kwargs, activation="leakyrelu")(var_x)
        var_x = Conv2DBlock(encoder_complexity,
                            normalization="instance",
                            activation="leakyrelu",
                            **kwargs)(var_x)
        var_x = Conv2DBlock(encoder_complexity * 2,
                            normalization="instance",
                            activation="leakyrelu",
                            **kwargs)(var_x)
        var_x = Conv2DBlock(encoder_complexity * 4, **kwargs, activation="leakyrelu")(var_x)
        var_x = Conv2DBlock(encoder_complexity * 6, **kwargs, activation="leakyrelu")(var_x)
        var_x = Conv2DBlock(encoder_complexity * 8, **kwargs, activation="leakyrelu")(var_x)
        var_x = Conv2DBlock(encoder_complexity,
                            normalization="instance",
                            activation="leakyrelu",
                            **kwargs)(var_x)
        var_x = Conv2DBlock(encoder_complexity * 2,
                            normalization="instance",
                            activation="leakyrelu",
                            **kwargs)(var_x)
        var_x = Conv2DBlock(encoder_complexity * 4, **kwargs, activation="leakyrelu")(var_x)
        var_x = Conv2DBlock(encoder_complexity * 6, **kwargs, activation="leakyrelu")(var_x)
        var_x = Conv2DBlock(encoder_complexity * 8, **kwargs, activation="leakyrelu")(var_x)
        var_x = Conv2DBlock(encoder_complexity,
                            normalization="instance",
                            activation="leakyrelu",
                            **kwargs)(var_x)
        var_x = Conv2DBlock(encoder_complexity * 2,
                            normalization="instance",
                            activation="leakyrelu",
                            **kwargs)(var_x)
        var_x = Conv2DBlock(encoder_complexity * 4, **kwargs, activation="leakyrelu")(var_x)
        var_x = Conv2DBlock(encoder_complexity * 6, **kwargs, activation="leakyrelu")(var_x)
        var_x = Conv2DBlock(encoder_complexity * 8, **kwargs, activation="leakyrelu")(var_x)
          var_x = Dense(self.encoder_dim,
                        kernel_initializer=self.kernel_initializer)(Flatten()(var_x))
          var_x = Dense(dense_shape * dense_shape * dense_dim,
                        kernel_initializer=self.kernel_initializer)(var_x)
          var_x = Reshape((dense_shape, dense_shape, dense_dim))(var_x)
keep keep keep keep replace replace replace replace replace keep keep keep keep keep
 lib.model - Maintenance
  - Add Depthwise option to Conv Block
  - Add Swish Activation function
  - remove res_block_follows and add_instance_norm_args
  - Add explicit normalization and activation args
  - Add K.resize_images layer
plugins.train.model - Inference creation bugfix 
plugins/train/model/unbalanced.py