def __init__(self, aggregator: af.Aggregator, settings_imaging: Optional[aa.SettingsImaging]=None, settings_pixelization: Optional[aa.SettingsPixelization]=None, settings_inversion: Optional[aa.SettingsInversion]=None, use_preloaded_grid: bool=True, use_hyper_scaling: bool=True):
    '\n        Wraps a PyAutoFit aggregator in order to create generators of fits to imaging data, corresponding to the\n        results of a non-linear search model-fit.\n        '
    super().__init__(aggregator=aggregator)
    self.settings_imaging = settings_imaging
    self.settings_pixelization = settings_pixelization
    self.settings_inversion = settings_inversion
    self.use_preloaded_grid = use_preloaded_grid
    self.use_hyper_scaling = use_hyper_scaling
Wraps a PyAutoFit aggregator in order to create generators of fits to imaging data, corresponding to the
results of a non-linear search model-fit.
def __init__(self, aggregator: af.Aggregator, settings_imaging: Optional[aa.SettingsImaging]=None, settings_pixelization: Optional[aa.SettingsPixelization]=None, settings_inversion: Optional[aa.SettingsInversion]=None, use_preloaded_grid: bool=True, use_hyper_scaling: bool=True):
    '\n        Wraps a PyAutoFit aggregator in order to create generators of fits to imaging data, corresponding to the\n        results of a non-linear search model-fit.\n        '
    super().__init__(aggregator=aggregator)
    self.settings_imaging = settings_imaging
    self.settings_pixelization = settings_pixelization
    self.settings_inversion = settings_inversion
    self.use_preloaded_grid = use_preloaded_grid
    self.use_hyper_scaling = use_hyper_scaling