          logger.trace("Randomly warping batch")  # type: ignore
          slices = self._constants.warp_slices
          rands = np.random.normal(size=(self._batchsize, 2, 5, 5),
                                   scale=self._warp_scale).astype("float32")
          batch_maps = ne.evaluate("m + r", local_dict=dict(m=self._constants.warp_maps, r=rands))
          batch_interp = np.array([[cv2.resize(map_, self._constants.warp_pad)[slices, slices]
                                    for map_ in maps]
                                   for maps in batch_maps])
          warped_batch = np.array([cv2.remap(image, interp[0], interp[1], cv2.INTER_LINEAR)
                                   for image, interp in zip(batch, batch_interp)])
 
</s> remove                  kernel_size: T.Union[int, T.Tuple[int, int]] = 5,
                 strides: T.Union[int, T.Tuple[int, int]] = 2,
 </s> add                  kernel_size: int | tuple[int, int] = 5,
                 strides: int | tuple[int, int] = 2, </s> remove         self.source: List[ConvertItem] = []  # Source images, filenames + detected faces
 </s> add         self.source: list[ConvertItem] = []  # Source images, filenames + detected faces </s> remove                  kernel_size: T.Union[int, T.Tuple[int, int]] = 5,
                 strides: T.Union[int, T.Tuple[int, int]] = 2, **kwargs) -> None:
 </s> add                  kernel_size: int | tuple[int, int] = 5,
                 strides: int | tuple[int, int] = 2, **kwargs) -> None: </s> remove                  normalization: T.Optional[str] = None,
                 activation: T.Optional[str] = "leakyrelu",
 </s> add                  normalization: str | None = None,
                 activation: str | None = "leakyrelu", </s> remove         self.destination: List[np.ndarray] = []  # Swapped + patched images
 </s> add         self.destination: list[np.ndarray] = []  # Swapped + patched images
        batch_maps = ne.evaluate("m + r", local_dict={"m": self._constants.warp_maps, "r": rands})
          logger.trace("Randomly warping batch")  # type: ignore
          slices = self._constants.warp_slices
          rands = np.random.normal(size=(self._batchsize, 2, 5, 5),
                                   scale=self._warp_scale).astype("float32")
        batch_maps = ne.evaluate("m + r", local_dict={"m": self._constants.warp_maps, "r": rands})
          batch_interp = np.array([[cv2.resize(map_, self._constants.warp_pad)[slices, slices]
                                    for map_ in maps]
                                   for maps in batch_maps])
          warped_batch = np.array([cv2.remap(image, interp[0], interp[1], cv2.INTER_LINEAR)
                                   for image, interp in zip(batch, batch_interp)])
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/augmentation.py