          logger.debug("Calculating minimum scale for screen dimensions %s", self._screen_dimensions)
          half_screen = tuple(x // 2 for x in self._screen_dimensions)
          min_scales = (half_screen[0] / self._image.source.shape[1],
                        half_screen[1] / self._image.source.shape[0])
          min_scale = min(1.0, min(min_scales))
          min_scale = (ceil(min_scale * 10)) * 10
  
          eight_screen = tuple(x * 8 for x in self._screen_dimensions)
          max_scales = (eight_screen[0] / self._image.source.shape[1],
                        eight_screen[1] / self._image.source.shape[0])
 
</s> remove     def _generate_weights(cls, channel: Dict[str, float], domain: np.ndarray) -> tf.Tensor:
 </s> add     def _generate_weights(cls, channel: dict[str, float], domain: np.ndarray) -> tf.Tensor: </s> remove         retval = batch_size / np.diff(cast(np.ndarray, _SESSION.get_timestamps(self._session_id)))
 </s> add         retval = batch_size / np.diff(T.cast(np.ndarray,
                                             _SESSION.get_timestamps(self._session_id))) </s> remove         assert self._config["centering"] in get_args(CenteringType)
 </s> add         assert self._config["centering"] in T.get_args(CenteringType) </s> remove             x_center = (cast(int, face.left) + face.right) / 2.0
            y_center = (cast(int, face.top) + face.bottom) / 2.0 - cast(int, face.height) * 0.12
            scale = (cast(int, face.width) + cast(int, face.height)) * self.reference_scale
            center_scale[index, :, 0] = np.full(68, x_center, dtype='float32')
            center_scale[index, :, 1] = np.full(68, y_center, dtype='float32')
 </s> add             x_ctr = (T.cast(int, face.left) + face.right) / 2.0
            y_ctr = (T.cast(int, face.top) + face.bottom) / 2.0 - T.cast(int, face.height) * 0.12
            scale = (T.cast(int, face.width) + T.cast(int, face.height)) * self.reference_scale
            center_scale[index, :, 0] = np.full(68, x_ctr, dtype='float32')
            center_scale[index, :, 1] = np.full(68, y_ctr, dtype='float32') </s> remove         rate: List[float] = []
 </s> add         rate: list[float] = []
        min_scale = min(1.0, *min_scales)
          logger.debug("Calculating minimum scale for screen dimensions %s", self._screen_dimensions)
          half_screen = tuple(x // 2 for x in self._screen_dimensions)
          min_scales = (half_screen[0] / self._image.source.shape[1],
                        half_screen[1] / self._image.source.shape[0])
        min_scale = min(1.0, *min_scales)
          min_scale = (ceil(min_scale * 10)) * 10
  
          eight_screen = tuple(x * 8 for x in self._screen_dimensions)
          max_scales = (eight_screen[0] / self._image.source.shape[1],
                        eight_screen[1] / self._image.source.shape[0])
keep keep keep keep replace keep keep keep keep keep
 Rebase code (#1326)

* Remove tensorflow_probability requirement

* setup.py - fix progress bars

* requirements.txt: Remove pre python 3.9 packages

* update apple requirements.txt

* update INSTALL.md

* Remove python<3.9 code

* setup.py - fix Windows Installer

* typing: python3.9 compliant

* Update pytest and readthedocs python versions

* typing fixes

* Python Version updates
  - Reduce max version to 3.10
  - Default to 3.10 in installers
  - Remove incompatible 3.11 tests

* Update dependencies

* Downgrade imageio dep for Windows

* typing: merge optional unions and fixes

* Updates
  - min python version 3.10
  - typing to python 3.10 spec
  - remove pre-tf2.10 code
  - Add conda tests

* train: re-enable optimizer saving

* Update dockerfiles

* Update setup.py
  - Apple Conda deps to setup.py
  - Better Cuda + dependency handling

* bugfix: Patch logging to prevent Autograph errors

* Update dockerfiles

* Setup.py - Setup.py - stdout to utf-8

* Add more OSes to github Actions

* suppress mac-os end to end test 
lib/training/preview_tk.py