AOBaseConfig

class torchao.core.config.AOBaseConfig

If a workflow config inherits from this then quantize_ knows how to a apply it to a model. For example:

# user facing code
class WorkflowFooConfig(AOBaseConfig): ...
    # configuration for workflow `Foo` is defined here
    bar = 'baz'

# non user facing code
@register_quantize_module_handler(WorkflowFooConfig)
def _transform(
    mod: torch.nn.Module,
    config: WorkflowFooConfig,
) -> torch.nn.Module:
    # the transform is implemented here, usually a tensor sublass
    # weight swap or a module swap
    ...

# then, the user calls `quantize_` with a config, and `_transform` is called
# under the hood by `quantize_.