          """ Return the dense width and number of upscalers """
          output_size = self.config['output_size']
          sides = [(output_size // 2**n, n) for n in [4, 5] if (output_size // 2**n) < 10]
          closest = min([x * self._downscale_ratio for x, _ in sides],
                        key=lambda x: abs(x - self.config['input_size']))
          dense_width, upscalers_no = [(s, n) for s, n in sides
                                       if s * self._downscale_ratio == closest][0]
          logger.debug("dense_width: %s, upscalers_no: %s", dense_width, upscalers_no)
          return dense_width, upscalers_no
  
 
</s> remove         output_size = self.config['output_size']
 </s> add         output_size = self.config["output_size"] </s> remove         input_filters = self.config['complexity_encoder'] * 2**(self.downscalers_no-1)
        input_width = self.config['input_size'] // self._downscale_ratio
 </s> add         input_filters = self.config["complexity_encoder"] * 2**(self.downscalers_no-1)
        input_width = self.config["input_size"] // self._downscale_ratio </s> remove         input_filters = self.config['complexity_encoder'] * 2**(self.downscalers_no-1)
        input_width = self.config['input_size'] // self._downscale_ratio
 </s> add         input_filters = self.config["complexity_encoder"] * 2**(self.downscalers_no-1)
        input_width = self.config["input_size"] // self._downscale_ratio </s> remove         encoder_complexity = self.config['complexity_encoder']
 </s> add         encoder_complexity = self.config["complexity_encoder"] </s> remove         decoder_b_complexity = self.config['complexity_decoder']
 </s> add         decoder_b_complexity = self.config["complexity_decoder"]
                      key=lambda x: abs(x - self.config["input_size"]))
          """ Return the dense width and number of upscalers """
          output_size = self.config['output_size']
          sides = [(output_size // 2**n, n) for n in [4, 5] if (output_size // 2**n) < 10]
          closest = min([x * self._downscale_ratio for x, _ in sides],
                      key=lambda x: abs(x - self.config["input_size"]))
          dense_width, upscalers_no = [(s, n) for s, n in sides
                                       if s * self._downscale_ratio == closest][0]
          logger.debug("dense_width: %s, upscalers_no: %s", dense_width, upscalers_no)
          return dense_width, upscalers_no
  
keep keep keep keep replace 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/realface.py