  
      @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
              A tensor.
          """
  
          if data_format == "channels_first":
              x = tf.transpose(x, (0, 3, 1, 2))
  
 
</s> remove     def _postprocess_conv2d_output(x, data_format):
 </s> add     def _postprocess_conv2d_output(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     def _preprocess_conv2d_input(x, data_format):
 </s> add     def _preprocess_conv2d_input(input_tensor, data_format): </s> remove             x = tf.cast(x, "float64")
        return x
 </s> add             input_tensor = tf.cast(input_tensor, "float64")
        return input_tensor </s> remove             x = tf.transpose(x, (0, 3, 1, 2))
 </s> add             input_tensor = tf.transpose(input_tensor, (0, 3, 1, 2))
        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
  
      @staticmethod
      def _postprocess_conv2d_output(x, data_format):
          """Transpose and cast the output from conv2d if needed.
        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
        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
        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
        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
        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
          """
  
          if data_format == "channels_first":
              x = tf.transpose(x, (0, 3, 1, 2))
  
keep keep keep keep replace replace replace replace 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