                       self._name, filters, kernel_size, padding, kwargs)
          self._use_reflect_padding = _CONFIG["reflect_padding"]
  
          self._filters = filters
          self._kernel_size = kernel_size
          self._padding = "valid" if self._use_reflect_padding else padding
          self._kwargs = kwargs
  
      def __call__(self, inputs: Tensor) -> Tensor:
          """ Call the Faceswap Residual Block.
 
</s> remove         if self.data_format == 'channels_first':
            batch_size, channels, height, width = input_shape
            if batch_size is None:
                batch_size = -1
            r_height, r_width = self.size
            o_height, o_width = height * r_height, width * r_width
            o_channels = channels // (r_height * r_width)
 </s> add     def call(self, inputs: tf.Tensor, *args, **kwargs) -> tf.Tensor:
        """ Override to call the layer. </s> remove         self.input_spec = keras.layers.InputSpec(ndim=4)
 </s> add  </s> remove     @classmethod
    def _depth_to_space(cls, ipt, scale, data_format=None):
        """ Uses phase shift algorithm to convert channels/depth for spatial resolution """
        if data_format is None:
            data_format = K.image_data_format()
        data_format = data_format.lower()
        ipt = cls._preprocess_conv2d_input(ipt, data_format)
        out = tf.nn.depth_to_space(ipt, scale)
        out = cls._postprocess_conv2d_output(out, data_format)
 </s> add             out = K.reshape(inputs, (batch_size, height, width, r_height, r_width, o_channels))
            out = K.permute_dimensions(out, (0, 1, 3, 2, 4, 5))
            out = K.reshape(out, (batch_size, o_height, o_width, o_channels))
        self._kernel_size = (kernel_size,
                             kernel_size) if isinstance(kernel_size, int) else kernel_size
                       self._name, filters, kernel_size, padding, kwargs)
          self._use_reflect_padding = _CONFIG["reflect_padding"]
  
          self._filters = filters
        self._kernel_size = (kernel_size,
                             kernel_size) if isinstance(kernel_size, int) else kernel_size
          self._padding = "valid" if self._use_reflect_padding else padding
          self._kwargs = kwargs
  
      def __call__(self, inputs: Tensor) -> Tensor:
          """ Call the Faceswap Residual Block.
keep keep keep keep replace keep keep keep keep keep
 Training fixes:
  - Fix mixed precision layer storing and switching
  - typofix in _requirements_base.txt
  - lib.model.layers - cleanup, docs and tests fixes
  - lib.model.nn_blocks - typing fixes 
lib/model/nn_blocks.py