input: Write a function to decode LibSVM input to tensors using TensorFlow operations. The function should be named `decode_libsvm` and should take the following arguments: `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(tf.constant(["1 1:1 2:2", "2 1:3 2:4"]), 2, dtype=tf.float32, label_dtype=tf.int64)==(array([1, 2], dtype=int64), array([[0, 1], [0, 2], [1, 1], [1, 2]]), array([1., 2., 3., 4.], dtype=float32), array([2, 2], dtype=int64))
  assert decode_libsvm(tf.constant(["1 1:1", "2 1:3"]), 1, dtype=tf.float32, label_dtype=tf.int64)==(array([1, 2], dtype=int64), array([[0, 1], [1, 1]]), array([1., 3.], dtype=float32), array([2, 1], dtype=int64))
  assert decode_libsvm(tf.constant([]), 1)==(array([], dtype=int64), array([], dtype=int64), array([], dtype=float32), array([0, 1], dtype=int64))
  assert decode_libsvm(tf.constant(["1 1:1", "2 1:3"]), 2, dtype=tf.float64, label_dtype=tf.float64)==(array([1, 2], dtype=float64), array([[0, 1], [1, 1]]), array([1., 3.], dtype=float64), array([2, 2], dtype=int64))
  assert decode_libsvm(tf.constant(["1 1:1"]), 1, dtype=tf.int32, label_dtype=tf.int32)==(array([1], dtype=int32), array([[0, 1]]), array([1], dtype=int32), array([1, 1], dtype=int64))
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, val = tf.strings.split(part, ':')
              indices.append([tf.strings.to_number(idx, out_type=tf.int64) - 1])
              values.append(tf.strings.to_number(val, out_type=dtype))
          return label, tf.stack(indices), tf.stack(values)
  
      labels, indices, values = tf.map_fn(parse_line, input, fn_output_signature=(label_dtype, tf.int64, dtype))
      labels = tf.reshape(labels, [-1])
      indices = tf.reshape(indices, [-1, 1])
      values = tf.reshape(values, [-1])
      feature_shape = tf.constant([tf.shape(input)[0], num_features], dtype=tf.int64)
      return labels, indices, values, feature_shape