              inp = ReflectionPadding2D(stride=strides,
                                        kernel_size=kernel_size,
                                        name="{}_reflectionpadding2d".format(name))(inp)
              padding = "valid"
          var_x = self.conv2d(inp, filters,
                              kernel_size=kernel_size,
                              strides=strides,
                              padding=padding,
                              name="{}_conv2d".format(name),
                              **kwargs)
 
</s> remove             inp = ReflectionPadding2D(stride=strides,
                                      kernel_size=kernel_size,
                                      name="{}_reflectionpadding2d".format(name))(inp)
 </s> add             input_tensor = ReflectionPadding2D(
                stride=strides,
                kernel_size=kernel_size,
                name="{}_reflectionpadding2d".format(name))(input_tensor) </s> remove             original_init = self.switch_kernel_initializer(
 </s> add             original_init = self._switch_kernel_initializer( </s> remove         kwargs = self.set_default_initializer(kwargs)
 </s> add         kwargs = self._set_default_initializer(kwargs) </s> remove         var_x = self.conv2d(inp, filters * scale_factor * scale_factor,
 </s> add         var_x = self.conv2d(input_tensor, filters * scale_factor * scale_factor, </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)
        var_x = self.conv2d(input_tensor, filters,
              inp = ReflectionPadding2D(stride=strides,
                                        kernel_size=kernel_size,
                                        name="{}_reflectionpadding2d".format(name))(inp)
              padding = "valid"
        var_x = self.conv2d(input_tensor, filters,
                              kernel_size=kernel_size,
                              strides=strides,
                              padding=padding,
                              name="{}_conv2d".format(name),
                              **kwargs)
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