          return self.sessions[self.session_id]
  
      def new_session_id(self):
          """ Return new session_id """
          if not self.sessions:
              session_id = 1
          else:
 
</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"]) </s> remove         var_x = Conv2D(3, kernel_size=5, padding='same', activation='sigmoid')(var_x)
 </s> add         var_x = self.blocks.conv2d(var_x, 3, kernel_size=5, padding="same", activation="sigmoid") </s> remove                       key=lambda x: abs(x - self.config['input_size']))
 </s> add                       key=lambda x: abs(x - self.config["input_size"])) </s> remove     def upscale(self, inp, filters, kernel_size=3, padding= 'same', use_instance_norm=False, res_block_follows=False, **kwargs):
 </s> add     def upscale(self, inp, filters, kernel_size=3, padding="same",
                use_instance_norm=False, res_block_follows=False, **kwargs): </s> remove             padding = 'valid'
        var_x = Conv2D(filters,
                       kernel_size=kernel_size,
                       strides=strides,
                       padding=padding,
                       **kwargs)(inp)
 </s> add             padding = "valid"
        var_x = self.conv2d(inp, filters,
                            kernel_size=kernel_size,
                            strides=strides,
                            padding=padding,
                            **kwargs)
    @property
    def first_run(self):
        """ Return True if this is the first run else False """
        return self.session_id == 1

          return self.sessions[self.session_id]
  
    @property
    def first_run(self):
        """ Return True if this is the first run else False """
        return self.session_id == 1

      def new_session_id(self):
          """ Return new session_id """
          if not self.sessions:
              session_id = 1
          else:
keep add 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