input: Write a function to decode a LibSVM input string Tensor into a tuple of tensors representing the label, feature indices, feature values, and feature shape. The function 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 2:1.5", "2 1:2.0"]), 3, dtype=tf.float32, label_dtype=tf.int32, name="test") == (tf.constant([1, 2], dtype=tf.int32), tf.constant([[0, 2], [1, 1]], dtype=tf.int64), tf.constant([1.5, 2.0], dtype=tf.float32), tf.constant([2, 3], dtype=tf.int64))
  assert decode_libsvm(tf.constant(["3 1:3.0", "4 2:4.0"]), 5, dtype=tf.float64, label_dtype=tf.float64, name="test2") == (tf.constant([3.0, 4.0], dtype=tf.float64), tf.constant([[0, 1], [1, 2]], dtype=tf.int64), tf.constant([3.0, 4.0], dtype=tf.float64), tf.constant([2, 5], dtype=tf.int64))
  assert decode_libsvm(tf.constant([]), 1) == (tf.constant([], dtype=tf.int64, shape=(0,)), tf.constant([], dtype=tf.int64, shape=(0, 2)), tf.constant([], dtype=tf.float32, shape=(0,)), tf.constant([0, 1], 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)
          indices = []
          values = []
          for part in parts[1:]:
              idx, val = tf.strings.split(part, ':')
              indices.append(tf.strings.to_number(idx, out_type=tf.int64))
              values.append(tf.strings.to_number(val, 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.ragged.stack(indices_list).flat_values
      values = tf.ragged.stack(values_list).flat_values
      feature_shape = tf.stack([tf.shape(input, out_type=tf.int64)[0], num_features])
  
      return labels, indices, values, feature_shape