input: Write a function to decode a LibSVM input string tensor into separate tensors for labels and features using TensorFlow operations. The function should take `input` (a `Tensor` of type `string`), `num_features` (an `int` that is `>= 1`), `dtype` (an optional `tf.DType` from: `tf.float32, tf.float64, tf.int32, tf.int64`. Defaults to `tf.float32`), `label_dtype` (an optional `tf.DType` from: `tf.float32, tf.float64, tf.int32, tf.int64`. Defaults to `tf.int64`), and `name` (a name for the operation, optional). The function should return a tuple of `Tensor` objects `(label, feature_indices, feature_values, feature_shape)`, where `label` is a `Tensor` of type `label_dtype`, `feature_indices` is a `Tensor` of type `int64`, `feature_values` is a `Tensor` of type `dtype`, and `feature_shape` is a `Tensor` of type `int64`.
  
  assert decode_libsvm(["1 1:1 2:2", "2 1:3 2:4"], 2) == (array([1, 2]), array([[0, 0], [1, 0], [0, 1], [1, 1]]), array([1, 3, 2, 4]), array([2]))
  assert decode_libsvm(["1 1:1 2:2"], 2, dtype=tf.float64) == (array([1]), array([[0, 0], [0, 1]]), array([1.0, 2.0], dtype=float64), array([1]))
  assert decode_libsvm(["1 1:1 2:2"], 2, label_dtype=tf.int32) == (array([1], dtype=int32), array([[0, 0], [0, 1]]), array([1, 2]), array([1]))
  assert decode_libsvm([], 1) == (array([], shape=(0,)), array([], shape=(0, 1)), array([], shape=(0,)), array([0]))
output: def decode_libsvm(input, num_features, dtype=tf.float32, label_dtype=tf.int64, name=None):
      def parse_line(line):
          parts = tf.strings.split(line, ' ')
          label = tf.strings.to_number(parts[0], out_type=label_dtype)
          indices = []
          values = []
          for part in parts[1:]:
              idx, value = tf.strings.split(part, ':')
              indices.append([tf.strings.to_number(idx, out_type=tf.int64)])
              values.append(tf.strings.to_number(value, out_type=dtype))
          return label, indices, values
  
      labels, indices_list, values_list = tf.map_fn(
          parse_line, input, dtype=(label_dtype, tf.int64, dtype),
          infer_shape=False, name=name
      )
  
      labels = tf.stack(labels)
      indices = tf.reshape(tf.concat(indices_list, axis=0), [-1, 1])
      values = tf.concat(values_list, axis=0)
      shape = tf.shape(labels, out_type=tf.int64)
  
      return labels, indices, values, shape