          self.trainer = trainer
  
          self.state = State(self.model_dir,
                             self.name,
                             self.config_changeable_items,
                             no_logs,
 
</s> remove         self.blocks = NNBlocks(use_subpixel=self.config["subpixel_upscaling"],
                               use_icnr_init=self.config["icnr_init"],
                               use_reflect_padding=self.config["reflect_padding"])
 </s> add  </s> remove             padding = 'valid'
        var_x = Conv2D(filters,
                       kernel_size=kernel_size,
                       padding=padding,
                       **kwargs)(var_x)
 </s> add             padding = "valid"
        var_x = self.conv2d(inp, filters,
                            kernel_size=kernel_size,
                            padding=padding,
                            **kwargs) </s> remove         dense_nodes = int(self.config['dense_nodes']/1.5)
 </s> add         dense_nodes = int(self.config["dense_nodes"]/1.5) </s> remove         var_xy = Dense(self.config['dense_nodes'])(Flatten()(var_xy))
 </s> add         var_xy = Dense(self.config["dense_nodes"])(Flatten()(var_xy))
        self.load_config()  # Load config if plugin has not already referenced it
          self.trainer = trainer
  
        self.load_config()  # Load config if plugin has not already referenced it
          self.state = State(self.model_dir,
                             self.name,
                             self.config_changeable_items,
                             no_logs,
keep add 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 
plugins/train/model/_base.py