                                          kernel_size=kernel_size,
                                          name="{}_reflectionpadding2d_1".format(name))(var_x)
              padding = "valid"
          if not self.use_convaware_init:
              original_init = self.switch_kernel_initializer(kwargs, VarianceScaling(
                  scale=0.2,
                  mode="fan_in",
                  distribution="uniform"))
          var_x = self.conv2d(var_x, filters,
                              kernel_size=kernel_size,
 
</s> remove             self.switch_kernel_initializer(kwargs, original_init)
        var_x = Add()([var_x, inp])
 </s> add             self._switch_kernel_initializer(kwargs, original_init)
        var_x = Add()([var_x, input_tensor]) </s> remove             original_init = self.switch_kernel_initializer(
 </s> add             original_init = self._switch_kernel_initializer( </s> remove         var_x = self.conv2d(inp, filters,
 </s> add         var_x = self.conv2d(input_tensor, filters, </s> remove             inp = ReflectionPadding2D(stride=1,
                                      kernel_size=kernel_size,
                                      name="{}_reflectionpadding2d".format(name))(inp)
 </s> add             input_tensor = ReflectionPadding2D(
                stride=1,
                kernel_size=kernel_size,
                name="{}_reflectionpadding2d".format(name))(input_tensor) </s> remove         kwargs = self.set_default_initializer(kwargs)
 </s> add         kwargs = self._set_default_initializer(kwargs)
            original_init = self._switch_kernel_initializer(kwargs, VarianceScaling(
                                          kernel_size=kernel_size,
                                          name="{}_reflectionpadding2d_1".format(name))(var_x)
              padding = "valid"
          if not self.use_convaware_init:
            original_init = self._switch_kernel_initializer(kwargs, VarianceScaling(
                  scale=0.2,
                  mode="fan_in",
                  distribution="uniform"))
          var_x = self.conv2d(var_x, filters,
                              kernel_size=kernel_size,
keep keep keep keep replace keep keep keep keep keep
 Simple backend unit tests (#1020)

* Add simple backend tests for lib.model
* Document lib.model
* Fix GMSD Loss for AMD
* Remove obsolete code from lib.model 
lib/model/nn_blocks.py