def METHOD_NAME(
    name,
    root,
    shuffle,
    batch_size,
    python_multiprocessing,
    num_parallel_workers,
    drop_remainder,
    args,
    num_shards=1,
    shard_id=0,
    is_training=True,
):
    """Create SSD dataset with MindDataset."""
    if name == "coco":
        if is_training:
            mindrecord_file = os.path.join(root, "train", "coco0")
        else:
            mindrecord_file = os.path.join(root, "val", "coco0")
        ds = de.MindDataset(
            mindrecord_file,
            columns_list=["img_id", "image", "annotation"],
            num_shards=num_shards,
            shard_id=shard_id,
            num_parallel_workers=num_parallel_workers,
            shuffle=shuffle,
        )
        decode = de.vision.Decode()
        ds = ds.map(operations=decode, input_columns=["image"])
        change_swap_op = de.vision.HWC2CHW()
        # Computed from random subset of ImageNet training images
        normalize_op = de.vision.Normalize(
            mean=[0.485 * 255, 0.456 * 255, 0.406 * 255], std=[0.229 * 255, 0.224 * 255, 0.225 * 255]
        )
        color_adjust_op = de.vision.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4)
        def compose_map_func(img_id, image, annotation):
            return preprocess_fn(img_id, image, annotation, is_training, args)
        if is_training:
            output_columns = ["image", "box", "label", "num_match"]
            trans = [color_adjust_op, normalize_op, change_swap_op]
        else:
            output_columns = ["img_id", "image", "image_shape"]
            trans = [normalize_op, change_swap_op]
        ds = ds.map(
            operations=compose_map_func,
            input_columns=["img_id", "image", "annotation"],
            output_columns=output_columns,
            column_order=output_columns,
            python_multiprocessing=python_multiprocessing,
            num_parallel_workers=num_parallel_workers,
        )
        ds = ds.map(
            operations=trans,
            input_columns=["image"],
            python_multiprocessing=python_multiprocessing,
            num_parallel_workers=num_parallel_workers,
        )
        ds = ds.batch(batch_size, drop_remainder=drop_remainder)
        return ds
    else:
        raise NotImplementedError