input: Write a function to decode LibSVM input to tensors. 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", "2:2"], dtype=tf.string), 2) == (tf.constant([1, 2], dtype=tf.int64), tf.constant([[0, 1], [1, 2]], dtype=tf.int64), tf.constant([1.0, 2.0], dtype=tf.float32), tf.constant([2, 2], dtype=tf.int64))
  assert decode_libsvm(tf.constant(["1:1", "2:2"], dtype=tf.string), 2, dtype=tf.float64) == (tf.constant([1, 2], dtype=tf.int64), tf.constant([[0, 1], [1, 2]], dtype=tf.int64), tf.constant([1.0, 2.0], dtype=tf.float64), tf.constant([2, 2], dtype=tf.int64))
  assert decode_libsvm(tf.constant(["1:1", "2:2"], dtype=tf.string), 2, label_dtype=tf.float32) == (tf.constant([1, 2], dtype=tf.float32), tf.constant([[0, 1], [1, 2]], dtype=tf.int64), tf.constant([1.0, 2.0], dtype=tf.float32), tf.constant([2, 2], dtype=tf.int64))
  assert decode_libsvm(tf.constant(["1:1", "2:2"], dtype=tf.string), 2, dtype=tf.int32, label_dtype=tf.int32) == (tf.constant([1, 2], dtype=tf.int32), tf.constant([[0, 1], [1, 2]], dtype=tf.int64), tf.constant([1, 2], dtype=tf.int32), tf.constant([2, 2], dtype=tf.int64))
  assert decode_libsvm(tf.constant([], dtype=tf.string), 2) == (tf.constant([], dtype=tf.int64), tf.constant([], dtype=tf.int64), tf.constant([], dtype=tf.float32), tf.constant([0, 2], dtype=tf.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)
          features = parts[1:]
          indices = tf.strings.to_number(tf.strings.split(features, ':')[0], out_type=tf.int64)
          values = tf.strings.to_number(tf.strings.split(features, ':')[1], out_type=dtype)
          return label, indices, values
  
      labels, indices, values = tf.map_fn(parse_line, input, dtype=(label_dtype, tf.int64, dtype))
      labels = tf.reshape(labels, [-1])
      indices = tf.reshape(indices, [-1])
      values = tf.reshape(values, [-1])
  
      num_elements = tf.shape(indices)[0]
      feature_indices = tf.stack([tf.range(num_elements, dtype=tf.int64), indices], axis=1)
      feature_shape = tf.stack([tf.shape(input, out_type=tf.int64)[0], num_features])
  
      return labels, feature_indices, values, feature_shape
  