              Additional standard keras Layer keyword arguments
  
          Returns
          -------
          tensor
              A tensor or list/tuple of tensors
          """
          if isinstance(self.size, int):
              retval = K.resize_images(inputs,
                                       self.size,
 
</s> remove         if self.data_format == 'channels_last':
            pooled = K.std(inputs, axis=[1, 2])
        else:
            pooled = K.std(inputs, axis=[2, 3])
        return pooled

 </s> add  </s> remove         if self.data_format == 'channels_last':
            pooled = K.min(inputs, axis=[1, 2])
        else:
            pooled = K.min(inputs, axis=[2, 3])
        return pooled
 </s> add         if self.data_format == "channels_first":
            batch, channels, rows, columns = input_shape
            return (batch,
                    channels // (self.scale_factor ** 2),
                    rows * self.scale_factor,
                    columns * self.scale_factor)
        batch, rows, columns, channels = input_shape
        return (batch,
                rows * self.scale_factor,
                columns * self.scale_factor,
                channels // (self.scale_factor ** 2)) </s> remove     def get_config(self):
 </s> add     def get_config(self) -> dict[str, T.Any]:
        :class:`tf.Tensor`
              Additional standard keras Layer keyword arguments
  
          Returns
          -------
        :class:`tf.Tensor`
              A tensor or list/tuple of tensors
          """
          if isinstance(self.size, int):
              retval = K.resize_images(inputs,
                                       self.size,
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/layers.py