          self._patch_factor = patch_factor
          self._ave_spectrum = ave_spectrum
          self._log_matrix = log_matrix
          self._batch_matrix = batch_matrix
          self._dims: Tuple[int, int] = (0, 0)
  
      def _get_patches(self, inputs: tf.Tensor) -> tf.Tensor:
          """ Crop the incoming batch of images into patches as defined by :attr:`_patch_factor.
  
          Parameters
 
</s> remove     adjusted_matrix: Optional[np.ndarray] = None
    interpolators: Tuple[int, int] = (0, 0)
    cropped_roi: Dict[CenteringType, np.ndarray] = field(default_factory=dict)
    cropped_slices: Dict[CenteringType, Dict[Literal["in", "out"],
                                             Tuple[slice, slice]]] = field(default_factory=dict)
 </s> add     adjusted_matrix: np.ndarray | None = None
    interpolators: tuple[int, int] = (0, 0)
    cropped_roi: dict[CenteringType, np.ndarray] = field(default_factory=dict)
    cropped_slices: dict[CenteringType, dict[T.Literal["in", "out"],
                                             tuple[slice, slice]]] = field(default_factory=dict) </s> remove         self.rocm_version: Tuple[int, ...] = (0, 0, 0)
 </s> add         self.rocm_version: tuple[int, ...] = (0, 0, 0) </s> remove     tf_min: T.Tuple[int, int] = (2, 0)
    scaling: T.Tuple[int, int] = (0, 1)
 </s> add     tf_min: tuple[int, int] = (2, 0)
    scaling: tuple[int, int] = (0, 1) </s> remove     def _get_laplacian_pyramid(self, inputs: tf.Tensor) -> List[tf.Tensor]:
 </s> add     def _get_laplacian_pyramid(self, inputs: tf.Tensor) -> list[tf.Tensor]: </s> remove         self.rocm_version: Tuple[int, ...] = (0, 0, 0)
 </s> add         self.rocm_version: tuple[int, ...] = (0, 0, 0)
        self._dims: tuple[int, int] = (0, 0)
          self._patch_factor = patch_factor
          self._ave_spectrum = ave_spectrum
          self._log_matrix = log_matrix
          self._batch_matrix = batch_matrix
        self._dims: tuple[int, int] = (0, 0)
  
      def _get_patches(self, inputs: tf.Tensor) -> tf.Tensor:
          """ Crop the incoming batch of images into patches as defined by :attr:`_patch_factor.
  
          Parameters
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/model/losses/loss.py