How to use the batchgenerators.transforms.utility_transforms.NumpyToTensor function in batchgenerators

scale=(0.85, 1.25), p_scale_per_sample=0.8,
                                          border_mode_data="nearest", border_mode_seg="nearest"),
                         ]


    elif mode == "val":
        tranform_list = [# CenterCropTransform(crop_size=target_size),
                         ResizeTransform(target_size=target_size, order=1),
                         ]

    elif mode == "test":
        tranform_list = [# CenterCropTransform(crop_size=target_size),
                         ResizeTransform(target_size=target_size, order=1),
                         ]

    tranform_list.append(NumpyToTensor())

    return Compose(tranform_list)

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)

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)

angle_x=(-0.1, 0.1), angle_y=(0, 1e-8), angle_z=(0, 1e-8),
                                           scale=(0.9, 1.4),
                                           border_mode_data="nearest", border_mode_seg="nearest"),
                          ]

    elif mode == "val":
        transform_list = [CenterCropTransform(crop_size=target_size),
                          ResizeTransform(target_size=target_size, order=1),
                          ]

    elif mode == "test":
        transform_list = [CenterCropTransform(crop_size=target_size),
                          ResizeTransform(target_size=target_size, order=1),
                          ]

    transform_list.append(NumpyToTensor())

    return Compose(transform_list)