input: Write a function to decode LibSVM input into tensors using TensorFlow. The function `decode_libsvm` should accept 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(["1 2:2 4:3"], 5, tf.float32, tf.int64) == (tf.constant([1], dtype=tf.int64), tf.constant([[2], [4]], dtype=tf.int64), tf.constant([2.0, 3.0], dtype=tf.float32), tf.constant([5], dtype=tf.int64))
  assert decode_libsvm(["2 1:1.5 3:2.5"], 5, tf.float32, tf.int64) == (tf.constant([2], dtype=tf.int64), tf.constant([[1], [3]], dtype=tf.int64), tf.constant([1.5, 2.5], dtype=tf.float32), tf.constant([5], dtype=tf.int64))
  assert decode_libsvm([""], 5, tf.float32, tf.int64) == (tf.constant([], dtype=tf.int64), tf.constant([], dtype=tf.int64), tf.constant([], dtype=tf.float32), tf.constant([5], dtype=tf.int64))
  assert decode_libsvm(["1 2:2 4:3"], 5, tf.int32, tf.float32) == (tf.constant([1], dtype=tf.float32), tf.constant([[2], [4]], dtype=tf.int64), tf.constant([2, 3], dtype=tf.int32), tf.constant([5], dtype=tf.int64))
output: def decode_libsvm(input, num_features, dtype=tf.float32, label_dtype=tf.int64, name=None):
      def parse_libsvm(line):
          tokens = tf.strings.split(line, ' ')
          label = tf.strings.to_number(tokens[0], out_type=label_dtype)
          indices = []
          values = []
          for token in tokens[1:]:
              idx, val = tf.strings.split(token, ':')
              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_libsvm, input, dtype=(label_dtype, tf.int64, dtype), name=name
      )
  
      feature_indices = tf.ragged.stack(indices_list).flat_values
      feature_values = tf.ragged.stack(values_list).flat_values
      feature_shape = tf.constant([num_features], dtype=tf.int64)
  
      return labels, feature_indices, feature_values, feature_shape