def METHOD_NAME(downloaded_data_dir):
    """
    REMEMBER TO CONVERT LABELS BACK TO BRATS CONVENTION AFTER PREDICTION!
    """
    task_name = "Task043_BraTS2019"
    print(task_name)
    print(downloaded_data_dir)
    print(nnUNet_raw_data)
    target_base = join(nnUNet_raw_data, task_name)
    if not os.path.isdir(target_base):
        target_imagesTr = join(target_base, "imagesTr")
        target_imagesVal = join(target_base, "imagesVal")
        target_imagesTs = join(target_base, "imagesTs")
        target_labelsTr = join(target_base, "labelsTr")
        maybe_mkdir_p(target_imagesTr)
        maybe_mkdir_p(target_imagesVal)
        maybe_mkdir_p(target_imagesTs)
        maybe_mkdir_p(target_labelsTr)
        patient_names = []
        for tpe in ["HGG", "LGG"]:
            cur = join(downloaded_data_dir, tpe)
            for p in subdirs(cur, join=False):
                patdir = join(cur, p)
                patient_name = tpe + "__" + p
                patient_names.append(patient_name)
                t1 = join(patdir, p + "_t1.nii.gz")
                t1c = join(patdir, p + "_t1ce.nii.gz")
                t2 = join(patdir, p + "_t2.nii.gz")
                flair = join(patdir, p + "_flair.nii.gz")
                seg = join(patdir, p + "_seg.nii.gz")
                assert all([
                    isfile(t1),
                    isfile(t1c),
                    isfile(t2),
                    isfile(flair),
                    isfile(seg)
                ]), "%s" % patient_name
                shutil.copy(t1, join(target_imagesTr, patient_name + "_0000.nii.gz"))
                shutil.copy(t1c, join(target_imagesTr, patient_name + "_0001.nii.gz"))
                shutil.copy(t2, join(target_imagesTr, patient_name + "_0002.nii.gz"))
                shutil.copy(flair, join(target_imagesTr, patient_name + "_0003.nii.gz"))
                copy_BraTS_segmentation_and_convert_labels(seg, join(target_labelsTr, patient_name + ".nii.gz"))
        json_dict = OrderedDict()
        json_dict['name'] = "BraTS2019"
        json_dict['description'] = "nothing"
        json_dict['tensorImageSize'] = "4D"
        json_dict['reference'] = "see BraTS2019"
        json_dict['licence'] = "see BraTS2019 license"
        json_dict['release'] = "0.0"
        json_dict['modality'] = {
            "0": "T1",
            "1": "T1ce",
            "2": "T2",
            "3": "FLAIR"
        }
        json_dict['labels'] = {
            "0": "background",
            "1": "edema",
            "2": "non-enhancing",
            "3": "enhancing",
        }
        json_dict['numTraining'] = len(patient_names)
        json_dict['numTest'] = 0
        json_dict['training'] = [{'image': "./imagesTr/%s.nii.gz" % i, "label": "./labelsTr/%s.nii.gz" % i} for i in
                                patient_names]
        json_dict['test'] = []
        save_json(json_dict, join(target_base, "dataset.json"))
    print("DONE")