          -------
          list:
              The jobs to be undertaken split into phases that fit into GPU RAM
          """
          phases: List[List[str]] = []
          current_phase: List[str] = []
          available = cast(int, self._vram_stats["vram_free"])
          for phase in self._flow:
              num_plugins = len([p for p in current_phase if self._vram_per_phase[p] > 0])
              num_plugins += 1 if self._vram_per_phase[phase] > 0 else 0
              scaling = self._parallel_scaling.get(num_plugins, self._scaling_fallback)
              required = sum(self._vram_per_phase[p] for p in current_phase + [phase]) * scaling
 
</s> remove         if plugins_required + batch_required <= cast(int, self._vram_stats["vram_free"]):
 </s> add         if plugins_required + batch_required <= T.cast(int, self._vram_stats["vram_free"]): </s> remove         samples: List[np.ndarray] = []
 </s> add         samples: list[np.ndarray] = [] </s> remove                  if cast(int, self._config[f"{area}_multiplier"]) > 1]
 </s> add                  if T.cast(int, self._config[f"{area}_multiplier"]) > 1] </s> remove             alignments = [cast("AlignmentFileDict", metadata["alignments"])]
 </s> add             alignments = [T.cast("AlignmentFileDict", metadata["alignments"])] </s> remove         retval: List[List[str]] = []
 </s> add         retval: list[list[str]] = []
        phases: list[list[str]] = []
        current_phase: list[str] = []
        available = T.cast(int, self._vram_stats["vram_free"])
          -------
          list:
              The jobs to be undertaken split into phases that fit into GPU RAM
          """
        phases: list[list[str]] = []
        current_phase: list[str] = []
        available = T.cast(int, self._vram_stats["vram_free"])
        phases: list[list[str]] = []
        current_phase: list[str] = []
        available = T.cast(int, self._vram_stats["vram_free"])
        phases: list[list[str]] = []
        current_phase: list[str] = []
        available = T.cast(int, self._vram_stats["vram_free"])
          for phase in self._flow:
              num_plugins = len([p for p in current_phase if self._vram_per_phase[p] > 0])
              num_plugins += 1 if self._vram_per_phase[phase] > 0 else 0
              scaling = self._parallel_scaling.get(num_plugins, self._scaling_fallback)
              required = sum(self._vram_per_phase[p] for p in current_phase + [phase]) * scaling
keep keep keep keep replace replace 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 
plugins/extract/pipeline.py