How to use the autofit.conf.instance.general.get function in autofit

def binned_hyper_galaxy_image_1d_path_dict(self, binned_grid):
        """
        A dictionary associating 1D hyper_galaxies galaxy cluster images with their names.
        """

        if binned_grid is not None:

            hyper_minimum_percent = af.conf.instance.general.get(
                "hyper", "hyper_minimum_percent", float
            )

            binned_image_1d_galaxy_dict = self.binned_image_1d_dict_from_binned_grid(
                binned_grid=binned_grid
            )

            binned_hyper_galaxy_image_path_dict = {}

            for path, galaxy in self.path_galaxy_tuples:
                binned_galaxy_image_1d = binned_image_1d_galaxy_dict[path]

                minimum_hyper_value = hyper_minimum_percent * max(
                    binned_galaxy_image_1d
                )
                binned_galaxy_image_1d[

def binned_hyper_galaxy_image_1d_path_dict(self, binned_grid):
        """
        A dictionary associating 1D hyper_galaxies galaxy cluster images with their names.
        """

        if binned_grid is not None:

            hyper_minimum_percent = af.conf.instance.general.get(
                "hyper", "hyper_minimum_percent", float
            )

            binned_image_1d_galaxy_dict = self.binned_image_1d_dict_from_binned_grid(
                binned_grid=binned_grid
            )

            binned_hyper_galaxy_image_path_dict = {}

            for path, galaxy in self.path_galaxy_tuples:
                binned_galaxy_image_1d = binned_image_1d_galaxy_dict[path]

                minimum_hyper_value = hyper_minimum_percent * max(
                    binned_galaxy_image_1d
                )
                binned_galaxy_image_1d[

def hyper_galaxy_image_1d_path_dict(self):
        """
        A dictionary associating 1D hyper_galaxies galaxy images with their names.
        """

        hyper_minimum_percent = af.conf.instance.general.get(
            "hyper", "hyper_minimum_percent", float
        )

        hyper_galaxy_image_1d_path_dict = {}

        for path, galaxy in self.path_galaxy_tuples:
            galaxy_image_1d = self.image_galaxy_1d_dict[path]

            minimum_galaxy_value = hyper_minimum_percent * max(galaxy_image_1d)
            galaxy_image_1d[
                galaxy_image_1d < minimum_galaxy_value
            ] = minimum_galaxy_value
            hyper_galaxy_image_1d_path_dict[path] = galaxy_image_1d

        return hyper_galaxy_image_1d_path_dict

    @property
    def hyper_galaxy_image_1d_path_dict(self):
        """
        A dictionary associating 1D hyper_galaxies galaxy images with their names.
        """

        hyper_minimum_percent = af.conf.instance.general.get(
            "hyper", "hyper_minimum_percent", float
        )

        hyper_galaxy_image_1d_path_dict = {}

        for path, galaxy in self.path_galaxy_tuples:
            galaxy_image_1d = self.image_galaxy_1d_dict[path]

            minimum_galaxy_value = hyper_minimum_percent * max(galaxy_image_1d)
            galaxy_image_1d[
                galaxy_image_1d < minimum_galaxy_value
            ] = minimum_galaxy_value
            hyper_galaxy_image_1d_path_dict[path] = galaxy_image_1d

        return hyper_galaxy_image_1d_path_dict

Signal-to_noise-map, etc).

    Set *autolens.datas.array.plotters.array_plotters* for a description of all innput parameters not described below.

    Parameters
    -----------
    image : datas.imaging.datas.Image
        The datas-datas, which includes the observed datas, noise_map-map, PSF, signal-to-noise_map-map, etc.
    plot_origin : True
        If true, the origin of the datas's coordinate system is plotted as a 'x'.
    """

    plot_imaging_image = conf.instance.general.get('output', 'plot_imaging_image', bool)
    plot_imaging_noise_map = conf.instance.general.get('output', 'plot_imaging_noise_map', bool)
    plot_imaging_psf = conf.instance.general.get('output', 'plot_imaging_psf', bool)
    plot_imaging_signal_to_noise_map = conf.instance.general.get('output', 'plot_imaging_signal_to_noise_map', bool)

    if plot_imaging_image:
        plot_image(image=image, plot_origin=plot_origin, mask=mask, positions=positions, output_path=output_path,
                   output_format=output_format)

    if plot_imaging_noise_map:
        plot_noise_map(image=image,  plot_origin=plot_origin, mask=mask, output_path=output_path,
                       output_format=output_format)

    if plot_imaging_psf:
        plot_psf(image=image, plot_origin=plot_origin, output_path=output_path, output_format=output_format)

    if plot_imaging_signal_to_noise_map:
        plot_signal_to_noise_map(image=image, plot_origin=plot_origin, mask=mask, output_path=output_path,
                                 output_format=output_format)

def plot_image_individual(image, plot_origin=True, mask=None, positions=None, output_path=None, output_format='png'):
    """Plot each attribute of the datas datas as individual figures one by one (e.g. the datas, noise_map-map, PSF, \
     Signal-to_noise-map, etc).

    Set *autolens.datas.array.plotters.array_plotters* for a description of all innput parameters not described below.

    Parameters
    -----------
    image : datas.imaging.datas.Image
        The datas-datas, which includes the observed datas, noise_map-map, PSF, signal-to-noise_map-map, etc.
    plot_origin : True
        If true, the origin of the datas's coordinate system is plotted as a 'x'.
    """

    plot_imaging_image = conf.instance.general.get('output', 'plot_imaging_image', bool)
    plot_imaging_noise_map = conf.instance.general.get('output', 'plot_imaging_noise_map', bool)
    plot_imaging_psf = conf.instance.general.get('output', 'plot_imaging_psf', bool)
    plot_imaging_signal_to_noise_map = conf.instance.general.get('output', 'plot_imaging_signal_to_noise_map', bool)

    if plot_imaging_image:
        plot_image(image=image, plot_origin=plot_origin, mask=mask, positions=positions, output_path=output_path,
                   output_format=output_format)

    if plot_imaging_noise_map:
        plot_noise_map(image=image,  plot_origin=plot_origin, mask=mask, output_path=output_path,
                       output_format=output_format)

    if plot_imaging_psf:
        plot_psf(image=image, plot_origin=plot_origin, output_path=output_path, output_format=output_format)

    if plot_imaging_signal_to_noise_map:
        plot_signal_to_noise_map(image=image, plot_origin=plot_origin, mask=mask, output_path=output_path,

def plot_image_individual(image, plot_origin=True, mask=None, positions=None, output_path=None, output_format='png'):
    """Plot each attribute of the datas datas as individual figures one by one (e.g. the datas, noise_map-map, PSF, \
     Signal-to_noise-map, etc).

    Set *autolens.datas.array.plotters.array_plotters* for a description of all innput parameters not described below.

    Parameters
    -----------
    image : datas.imaging.datas.Image
        The datas-datas, which includes the observed datas, noise_map-map, PSF, signal-to-noise_map-map, etc.
    plot_origin : True
        If true, the origin of the datas's coordinate system is plotted as a 'x'.
    """

    plot_imaging_image = conf.instance.general.get('output', 'plot_imaging_image', bool)
    plot_imaging_noise_map = conf.instance.general.get('output', 'plot_imaging_noise_map', bool)
    plot_imaging_psf = conf.instance.general.get('output', 'plot_imaging_psf', bool)
    plot_imaging_signal_to_noise_map = conf.instance.general.get('output', 'plot_imaging_signal_to_noise_map', bool)

    if plot_imaging_image:
        plot_image(image=image, plot_origin=plot_origin, mask=mask, positions=positions, output_path=output_path,
                   output_format=output_format)

    if plot_imaging_noise_map:
        plot_noise_map(image=image,  plot_origin=plot_origin, mask=mask, output_path=output_path,
                       output_format=output_format)

    if plot_imaging_psf:
        plot_psf(image=image, plot_origin=plot_origin, output_path=output_path, output_format=output_format)

    if plot_imaging_signal_to_noise_map:

"""Plot each attribute of the datas datas as individual figures one by one (e.g. the datas, noise_map-map, PSF, \
     Signal-to_noise-map, etc).

    Set *autolens.datas.array.plotters.array_plotters* for a description of all innput parameters not described below.

    Parameters
    -----------
    image : datas.imaging.datas.Image
        The datas-datas, which includes the observed datas, noise_map-map, PSF, signal-to-noise_map-map, etc.
    plot_origin : True
        If true, the origin of the datas's coordinate system is plotted as a 'x'.
    """

    plot_imaging_image = conf.instance.general.get('output', 'plot_imaging_image', bool)
    plot_imaging_noise_map = conf.instance.general.get('output', 'plot_imaging_noise_map', bool)
    plot_imaging_psf = conf.instance.general.get('output', 'plot_imaging_psf', bool)
    plot_imaging_signal_to_noise_map = conf.instance.general.get('output', 'plot_imaging_signal_to_noise_map', bool)

    if plot_imaging_image:
        plot_image(image=image, plot_origin=plot_origin, mask=mask, positions=positions, output_path=output_path,
                   output_format=output_format)

    if plot_imaging_noise_map:
        plot_noise_map(image=image,  plot_origin=plot_origin, mask=mask, output_path=output_path,
                       output_format=output_format)

    if plot_imaging_psf:
        plot_psf(image=image, plot_origin=plot_origin, output_path=output_path, output_format=output_format)

    if plot_imaging_signal_to_noise_map:
        plot_signal_to_noise_map(image=image, plot_origin=plot_origin, mask=mask, output_path=output_path,
                                 output_format=output_format)