我正在嘗試解析我的 tfrecord 數(shù)據(jù)集以將其用于對象檢測。當(dāng)我試圖將我的稀疏張量更改為密集張量時(shí)，出現(xiàn)以下我無法理解的錯(cuò)誤：ValueError: Shapes must be equal rank, but are 1 and 0    From merging shape 3 with other shapes. for '{{node stack}} = Pack[N=5, T=DT_FLOAT, axis=1](SparseToDense, SparseToDense_1, SparseToDense_2, SparseToDense_3, Cast)' with input shapes: [?], [?], [?], [?], [].我的 feature_description 是：feature_description = {    'image/filename': tf.io.FixedLenFeature([], tf.string),    'image/encoded': tf.io.FixedLenFeature([], tf.string),    'image/object/bbox/xmin': tf.io.VarLenFeature(tf.float32),    'image/object/bbox/ymin': tf.io.VarLenFeature(tf.float32),    'image/object/bbox/xmax': tf.io.VarLenFeature(tf.float32),    'image/object/bbox/ymax': tf.io.VarLenFeature(tf.float32),    'image/object/class/label': tf.io.VarLenFeature(tf.int64),}我的解析代碼：def _parse_image_function(example_proto):  # Parse the input tf.Example proto using the dictionary above.  return tf.io.parse_single_example(example_proto, feature_description)def _parse_tfrecord(x):      x_train = tf.image.decode_jpeg(x['image/encoded'], channels=3)    x_train = tf.image.resize(x_train, (416, 416))        labels = tf.cast(1, tf.float32)#    print(type(x['image/object/bbox/xmin']))    tf.print(x['image/object/bbox/xmin'])    y_train = tf.stack([tf.sparse.to_dense(x['image/object/bbox/xmin']),                        tf.sparse.to_dense(x['image/object/bbox/ymin']),                        tf.sparse.to_dense(x['image/object/bbox/xmax']),                        tf.sparse.to_dense(x['image/object/bbox/ymax']),                        labels], axis=1)    paddings = [[0, 100 - tf.shape(y_train)[0]], [0, 0]]    y_train = tf.pad(y_train, paddings)    return x_train, y_traindef load_tfrecord_dataset(train_record_file, size=416):    dataset=tf.data.TFRecordDataset(train_record_file)    parsed_dataset = dataset.map(_parse_image_function)    final = parsed_dataset.map(_parse_tfrecord)    return final我的錯(cuò)誤是什么？