          var_x = Conv2DBlock(dims * 8)(var_x)
          var_x = Dense(self.ae_dims)(Flatten()(var_x))
          var_x = Dense(lowest_dense_res * lowest_dense_res * self.ae_dims)(var_x)
          var_x = Reshape((lowest_dense_res, lowest_dense_res, self.ae_dims))(var_x)
          var_x = UpscaleBlock(self.ae_dims)(var_x)
          return KerasModel(input_, var_x, name="encoder_df")
  
      def encoder_liae(self):
          """ DFL SAE LIAE Encoder Network """
          input_ = Input(shape=self.input_shape)
 
</s> remove         var_x = Conv2DBlock(dims)(input_)
        var_x = Conv2DBlock(dims * 2)(var_x)
        var_x = Conv2DBlock(dims * 4)(var_x)
        var_x = Conv2DBlock(dims * 8)(var_x)
 </s> add         var_x = Conv2DBlock(dims, activation="leakyrelu")(input_)
        var_x = Conv2DBlock(dims * 2, activation="leakyrelu")(var_x)
        var_x = Conv2DBlock(dims * 4, activation="leakyrelu")(var_x)
        var_x = Conv2DBlock(dims * 8, activation="leakyrelu")(var_x) </s> remove         var_x = Conv2DBlock(dims)(input_)
        var_x = Conv2DBlock(dims * 2)(var_x)
        var_x = Conv2DBlock(dims * 4)(var_x)
        var_x = Conv2DBlock(dims * 8)(var_x)
 </s> add         var_x = Conv2DBlock(dims, activation="leakyrelu")(input_)
        var_x = Conv2DBlock(dims * 2, activation="leakyrelu")(var_x)
        var_x = Conv2DBlock(dims * 4, activation="leakyrelu")(var_x)
        var_x = Conv2DBlock(dims * 8, activation="leakyrelu")(var_x) </s> remove         var_x = UpscaleBlock(self.ae_dims * 2)(var_x)
 </s> add         var_x = UpscaleBlock(self.ae_dims * 2, activation="leakyrelu")(var_x) </s> remove         var_x = Conv2DBlock(128)(var_x)
        var_x = Conv2DBlock(256)(var_x)
        var_x = Conv2DBlock(512)(var_x)
 </s> add         var_x = Conv2DBlock(128, activation="leakyrelu")(var_x)
        var_x = Conv2DBlock(256, activation="leakyrelu")(var_x)
        var_x = Conv2DBlock(512, activation="leakyrelu")(var_x)
 </s> remove         var_x = Conv2DBlock(128)(input_)
        var_x = Conv2DBlock(256)(var_x)
        var_x = Conv2DBlock(512)(var_x)
        var_x = Conv2DBlock(1024)(var_x)
 </s> add         var_x = Conv2DBlock(128, activation="leakyrelu")(input_)
        var_x = Conv2DBlock(256, activation="leakyrelu")(var_x)
        var_x = Conv2DBlock(512, activation="leakyrelu")(var_x)
        var_x = Conv2DBlock(1024, activation="leakyrelu")(var_x)
        var_x = UpscaleBlock(self.ae_dims, activation="leakyrelu")(var_x)
          var_x = Conv2DBlock(dims * 8)(var_x)
          var_x = Dense(self.ae_dims)(Flatten()(var_x))
          var_x = Dense(lowest_dense_res * lowest_dense_res * self.ae_dims)(var_x)
          var_x = Reshape((lowest_dense_res, lowest_dense_res, self.ae_dims))(var_x)
        var_x = UpscaleBlock(self.ae_dims, activation="leakyrelu")(var_x)
          return KerasModel(input_, var_x, name="encoder_df")
  
      def encoder_liae(self):
          """ DFL SAE LIAE Encoder Network """
          input_ = Input(shape=self.input_shape)
keep keep keep keep 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/dfl_sae.py