          out = cls._postprocess_conv2d_output(out, data_format)
          return out
  
      @staticmethod
      def _postprocess_conv2d_output(x, data_format):
          """Transpose and cast the output from conv2d if needed.
          # Arguments
              x: A tensor.
              data_format: string, `"channels_last"` or `"channels_first"`.
          # Returns
 
</s> remove         # Arguments
            x: A tensor.
            data_format: string, `"channels_last"` or `"channels_first"`.
        # Returns
            A tensor.
 </s> add         Parameters
        ----------
        input_tensor: tensor
            The input that requires transposing and casting
        data_format: str
            `"channels_last"` or `"channels_first"`

        Returns
        -------
        tensor
            The transposed and cast input tensor </s> remove     def _preprocess_conv2d_input(x, data_format):
 </s> add     def _preprocess_conv2d_input(input_tensor, data_format): </s> remove         # Arguments
            x: input tensor.
            data_format: string, `"channels_last"` or `"channels_first"`.
        # Returns
            A tensor.
 </s> add         Parameters
        ----------
        input_tensor: tensor
            The input that requires transposing and casting
        data_format: str
            `"channels_last"` or `"channels_first"`

        Returns
        -------
        tensor
            The transposed and cast input tensor </s> remove             x = tf.cast(x, "float64")
        return x
 </s> add             input_tensor = tf.cast(input_tensor, "float64")
        return input_tensor </s> remove         if K.dtype(x) == "float64":
            x = tf.cast(x, "float32")
 </s> add         if K.dtype(input_tensor) == "float64":
            input_tensor = tf.cast(input_tensor, "float32")
    def _postprocess_conv2d_output(input_tensor, data_format):
          out = cls._postprocess_conv2d_output(out, data_format)
          return out
  
      @staticmethod
    def _postprocess_conv2d_output(input_tensor, data_format):
          """Transpose and cast the output from conv2d if needed.
          # Arguments
              x: A tensor.
              data_format: string, `"channels_last"` or `"channels_first"`.
          # Returns
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/layers.py