input: Write a function to use the `decode_libsvm` function provided in the TensorFlow API to decode a LibSVM input string to a tuple of tensors representing the label, feature indices, feature values, and feature shape. The function should accept the input string, the number of features, and optional dtypes for the label and features. The number of features must be greater than or equal to 1. The function should return the tuple of tensors.
  assert decode_libsvm_input("1 2:3 4:5", 5, dtype=tf.float32, label_dtype=tf.int32)==(tf.constant([1], dtype=tf.int32), tf.constant([[2, 4]], dtype=tf.int64), tf.constant([3.0, 5.0], dtype=tf.float32), tf.constant([5], dtype=tf.int64))
  assert decode_libsvm_input("2 1:4", 4, dtype=tf.float64, label_dtype=tf.int64)==(tf.constant([2], dtype=tf.int64), tf.constant([[1]], dtype=tf.int64), tf.constant([4.0], dtype=tf.float64), tf.constant([4], dtype=tf.int64))
  assert decode_libsvm_input("", 3, dtype=tf.int32, label_dtype=tf.int64)==(tf.constant([], dtype=tf.int64), tf.constant([[]], dtype=tf.int64), tf.constant([], dtype=tf.int32), tf.constant([3], dtype=tf.int64))
  assert decode_libsvm_input("0 1:2 3:4", 5)==(tf.constant([0], dtype=tf.int64), tf.constant([[1, 3]], dtype=tf.int64), tf.constant([2.0, 4.0], dtype=tf.float32), tf.constant([5], dtype=tf.int64))
  assert decode_libsvm_input("", 1, dtype=tf.float32, label_dtype=tf.float64)==(tf.constant([], dtype=tf.float64), tf.constant([[]], dtype=tf.int64), tf.constant([], dtype=tf.float32), tf.constant([1], dtype=tf.int64))
output: def decode_libsvm_input(input_string, num_features, dtype=tf.float32, label_dtype=tf.int64):
      if input_string == "":
          return (
              tf.constant([], dtype=label_dtype),
              tf.constant([[]], dtype=tf.int64),
              tf.constant([], dtype=dtype),
              tf.constant([num_features], dtype=tf.int64)
          )
      else:
          label, indices, values = tf.io.decode_libsvm(input_string, num_features)
          return (
              tf.cast(label, label_dtype),
              tf.expand_dims(indices, axis=0),
              tf.cast(values, dtype),
              tf.constant([num_features], dtype=tf.int64)
          )