          self._display = display
          self._loss_keys = loss_keys if isinstance(loss_keys, list) else [loss_keys]
          self._selections = selections if isinstance(selections, list) else [selections]
          self._is_totals = session_id is None
          self._args: Dict[str, Union[int, float]] = {"avg_samples": avg_samples,
                                                      "smooth_amount": smooth_amount,
                                                      "flatten_outliers": flatten_outliers}
          self._iterations = 0
          self._limit = 0
          self._start_iteration = 0
          self._stats: Dict[str, np.ndarray] = {}
          self.refresh()
 
</s> remove         self._stats: Dict[str, np.ndarray] = {}
 </s> add         self._stats: dict[str, np.ndarray] = {} </s> remove         seen: Dict[str, List[int]] = {}
 </s> add         seen: dict[str, list[int]] = {} </s> remove         self._threshold: Optional[float] = 0.25 if threshold < 0 else threshold
 </s> add         self._threshold: float | None = 0.25 if threshold < 0 else threshold </s> remove         predicted: T.List[np.ndarray] = inbound if isinstance(inbound, list) else [inbound]
 </s> add         predicted: list[np.ndarray] = inbound if isinstance(inbound, list) else [inbound] </s> remove         self._mixed_precision_layers: T.List[str] = []
 </s> add         self._mixed_precision_layers: list[str] = []
        self._args: dict[str, int | float] = {"avg_samples": avg_samples,
                                              "smooth_amount": smooth_amount,
                                              "flatten_outliers": flatten_outliers}
          self._display = display
          self._loss_keys = loss_keys if isinstance(loss_keys, list) else [loss_keys]
          self._selections = selections if isinstance(selections, list) else [selections]
          self._is_totals = session_id is None
        self._args: dict[str, int | float] = {"avg_samples": avg_samples,
                                              "smooth_amount": smooth_amount,
                                              "flatten_outliers": flatten_outliers}
        self._args: dict[str, int | float] = {"avg_samples": avg_samples,
                                              "smooth_amount": smooth_amount,
                                              "flatten_outliers": flatten_outliers}
        self._args: dict[str, int | float] = {"avg_samples": avg_samples,
                                              "smooth_amount": smooth_amount,
                                              "flatten_outliers": flatten_outliers}
          self._iterations = 0
          self._limit = 0
          self._start_iteration = 0
          self._stats: Dict[str, np.ndarray] = {}
          self.refresh()
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 
lib/gui/analysis/stats.py