          shape = list(shape)
          if self.scale == 1:
              return self.initializer(shape)
          new_shape = shape[:3] + [shape[3] // (self.scale ** 2)]
          if type(self.initializer) is dict:
              self.initializer = initializers.deserialize(self.initializer)
          var_x = self.initializer(new_shape, dtype)
          var_x = tf.transpose(var_x, perm=[2, 0, 1, 3])
          var_x = tf.image.resize_nearest_neighbor(
                           var_x,
 
</s> remove                          var_x,
                         size=(shape[0] * self.scale, shape[1] * self.scale),
                         align_corners=True)
 </s> add             var_x,
            size=(shape[0] * self.scale, shape[1] * self.scale),
            align_corners=True) </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         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     elif norm == 'groupnorm':
 </s> add     elif norm == "groupnorm": </s> remove     elif norm == 'hybrid':
 </s> add     elif norm == "hybrid":
        if isinstance(self.initializer, dict):
          shape = list(shape)
          if self.scale == 1:
              return self.initializer(shape)
          new_shape = shape[:3] + [shape[3] // (self.scale ** 2)]
        if isinstance(self.initializer, dict):
              self.initializer = initializers.deserialize(self.initializer)
          var_x = self.initializer(new_shape, dtype)
          var_x = tf.transpose(var_x, perm=[2, 0, 1, 3])
          var_x = tf.image.resize_nearest_neighbor(
                           var_x,
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 
lib/model/initializers.py