                             no_logs,
                             pingpong,
                             training_image_size)
          self.is_legacy = False
          self.rename_legacy()
          self.load_state_info()
  
          self.networks = dict()  # Networks for the model
          self.predictors = dict()  # Predictors for model
 
</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     @staticmethod
    def update_kwargs(kwargs):
        """ Set the default kernel initializer to he_uniform() """
        kwargs["kernel_initializer"] = kwargs.get("kernel_initializer", he_uniform())
 </s> add     def update_kwargs(self, kwargs):
        """ Update Kwargs for conv2D and Seperable conv2D layers.
            Set the default kernel initializer to conv_aware or he_uniform()
            if a specific initializer has not been passed in """
        if self.use_convaware_init:
            default = ConvolutionAware()
            if self.first_run:
                # Indicate the Convolutional Aware should be calculated on first run
                default._init = True  # pylint:disable=protected-access
        else:
            default = he_uniform()
        kwargs["kernel_initializer"] = kwargs.get("kernel_initializer", default)
        logger.debug("Set default kernel_initializer to: %s", kwargs["kernel_initializer"])
        self.blocks = NNBlocks(use_subpixel=self.config["subpixel_upscaling"],
                               use_icnr_init=self.config["icnr_init"],
                               use_convaware_init=self.config["conv_aware_init"],
                               use_reflect_padding=self.config["reflect_padding"],
                               first_run=self.state.first_run)

                             no_logs,
                             pingpong,
                             training_image_size)
        self.blocks = NNBlocks(use_subpixel=self.config["subpixel_upscaling"],
                               use_icnr_init=self.config["icnr_init"],
                               use_convaware_init=self.config["conv_aware_init"],
                               use_reflect_padding=self.config["reflect_padding"],
                               first_run=self.state.first_run)

          self.is_legacy = False
          self.rename_legacy()
          self.load_state_info()
  
          self.networks = dict()  # Networks for the model
          self.predictors = dict()  # Predictors for model
keep keep add keep 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 
plugins/train/model/_base.py