                             "likely because the weights you are trying to load were trained with "
                             "different settings than you have set for your current model.",
                             skipped_ops)
  
      def _get_weights_model(self) -> T.List[keras.models.Model]:
          """ Obtain a list of all sub-models contained within the weights model.
  
          Returns
          -------
          list
 
</s> remove     def get_argument_list() -> List[Dict[str, Any]]:
 </s> add     def get_argument_list() -> list[dict[str, T.Any]]: </s> remove     def build_model(self, inputs: T.List[Tensor]) -> keras.models.Model:
 </s> add     def build_model(self, inputs: list[Tensor]) -> keras.models.Model: </s> remove "Apply AutoClipping to the gradients. AutoClip analyzes the "
"gradient weights and adjusts the normalization value dynamically to fit the "
"data. Can help prevent NaNs and improve model optimization at the expense of "
"VRAM. Ref: AutoClip: Adaptive Gradient Clipping for Source Separation "
"Networks https://arxiv.org/abs/2007.14469"
 </s> add "When to save the Optimizer Weights. Saving the optimizer weights is not "
"necessary and will increase the model file size 3x (and by extension the "
"amount of time it takes to save the model). However, it can be useful to "
"save these weights if you want to guarantee that a resumed model carries off "
"exactly from where it left off, rather than spending a few hundred "
"iterations catching up.\n"
"\t never - Don't save optimizer weights.\n"
"\t always - Save the optimizer weights at every save iteration. Model saving "
"will take longer, due to the increased file size, but you will always have "
"the last saved optimizer state in your model file.\n"
"\t exit - Only save the optimizer weights when explicitly terminating a "
"model. This can be when the model is actively stopped or when the target "
"iterations are met. Note: If the training session ends because of another "
"reason (e.g. power outage, Out of Memory Error, NaN detected) then the "
"optimizer weights will NOT be saved." </s> remove     def _set_timelapse(self) -> Dict[Literal["input_a", "input_b", "output"], str]:
 </s> add     def _set_timelapse(self) -> dict[T.Literal["input_a", "input_b", "output"], str]: </s> remove                        layer: T.Union[keras.models.Model, keras.layers.Layer]) -> dict:
 </s> add                        layer: keras.models.Model | keras.layers.Layer) -> dict:
    def _get_weights_model(self) -> list[keras.models.Model]:
                             "likely because the weights you are trying to load were trained with "
                             "different settings than you have set for your current model.",
                             skipped_ops)
  
    def _get_weights_model(self) -> list[keras.models.Model]:
          """ Obtain a list of all sub-models contained within the weights model.
  
          Returns
          -------
          list
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 
plugins/train/model/_base/io.py