How to use the batchgenerators.transforms.noise_transforms.GaussianNoiseTransform function in batchgenerators

p_rot_per_sample=self.Config.P_SAMP,
                                                p_scale_per_sample=self.Config.P_SAMP))

                if self.Config.DAUG_RESAMPLE:
                    tfs.append(SimulateLowResolutionTransform(zoom_range=(0.5, 1), p_per_sample=0.2, per_channel=False))

                if self.Config.DAUG_RESAMPLE_LEGACY:
                    tfs.append(ResampleTransformLegacy(zoom_range=(0.5, 1)))

                if self.Config.DAUG_GAUSSIAN_BLUR:
                    tfs.append(GaussianBlurTransform(blur_sigma=self.Config.DAUG_BLUR_SIGMA,
                                                     different_sigma_per_channel=False,
                                                     p_per_sample=self.Config.P_SAMP))

                if self.Config.DAUG_NOISE:
                    tfs.append(GaussianNoiseTransform(noise_variance=self.Config.DAUG_NOISE_VARIANCE,
                                                      p_per_sample=self.Config.P_SAMP))

                if self.Config.DAUG_MIRROR:
                    tfs.append(MirrorTransform())

                if self.Config.DAUG_FLIP_PEAKS:
                    tfs.append(FlipVectorAxisTransform())

        tfs.append(NumpyToTensor(keys=["data", "seg"], cast_to="float"))

        #num_cached_per_queue 1 or 2 does not really make a difference
        batch_gen = MultiThreadedAugmenter(batch_generator, Compose(tfs), num_processes=num_processes,
                                           num_cached_per_queue=1, seeds=None, pin_memory=True)
        return batch_gen  # data: (batch_size, channels, x, y), seg: (batch_size, channels, x, y)

do_elastic_deform=self.Config.DAUG_ELASTIC_DEFORM,
                                                alpha=(90., 120.), sigma=(9., 11.),
                                                do_rotation=self.Config.DAUG_ROTATE,
                                                angle_x=(-0.8, 0.8), angle_y=(-0.8, 0.8), angle_z=(-0.8, 0.8),
                                                do_scale=True, scale=(0.9, 1.5), border_mode_data='constant',
                                                border_cval_data=0,
                                                order_data=3,
                                                border_mode_seg='constant', border_cval_seg=0,
                                                order_seg=0, random_crop=True, p_el_per_sample=0.2,
                                                p_rot_per_sample=0.2, p_scale_per_sample=0.2))

                if self.Config.DAUG_RESAMPLE:
                    tfs.append(SimulateLowResolutionTransform(zoom_range=(0.5, 1), p_per_sample=0.2))

                if self.Config.DAUG_NOISE:
                    tfs.append(GaussianNoiseTransform(noise_variance=(0, 0.05), p_per_sample=0.2))

                if self.Config.DAUG_MIRROR:
                    tfs.append(MirrorTransform())

                if self.Config.DAUG_FLIP_PEAKS:
                    tfs.append(FlipVectorAxisTransform())

        tfs.append(NumpyToTensor(keys=["data", "seg"], cast_to="float"))

        # num_cached_per_queue 1 or 2 does not really make a difference
        batch_gen = MultiThreadedAugmenter(batch_generator, Compose(tfs), num_processes=num_processes,
                                           num_cached_per_queue=1, seeds=None, pin_memory=True)
        return batch_gen  # data: (batch_size, channels, x, y), seg: (batch_size, channels, x, y)