input: Write a function to encode the input string in LibSVM format and return the corresponding TensorFlow tensor. The `encode_libsvm` 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 encode_libsvm(["1 1:2 2:3", "2 1:4"], 2, tf.float32, tf.int64) == (tf.constant([1, 2], dtype=tf.int64), tf.constant([[0, 0], [0, 1], [1, 0]], dtype=tf.int64), tf.constant([2.0, 3.0, 4.0], dtype=tf.float32), tf.constant([2, 2], dtype=tf.int64))
  assert encode_libsvm(["1 1:2"], 1, tf.float32, tf.int64) == (tf.constant([1], dtype=tf.int64), tf.constant([[0, 0]], dtype=tf.int64), tf.constant([2.0], dtype=tf.float32), tf.constant([1, 1], dtype=tf.int64))
  assert encode_libsvm([], 1, tf.float32, tf.int64) == (tf.constant([], dtype=tf.int64), tf.constant([], dtype=tf.int64), tf.constant([], dtype=tf.float32), tf.constant([0, 1], dtype=tf.int64))
output: def encode_libsvm(input, num_features, dtype=tf.float32, label_dtype=tf.int64, name=None):
      with tf.name_scope(name, "encode_libsvm", [input, num_features, dtype, label_dtype]):
          input = tf.convert_to_tensor(input, dtype=tf.string)
          split_lines = tf.strings.split(input, sep=' ')
          labels = tf.strings.to_number(split_lines.values[0::2], out_type=label_dtype)
          features = split_lines.values[1::2]
          feature_split = tf.strings.split(features, sep=':')
          indices = tf.strings.to_number(feature_split.values[0::2], out_type=tf.int64) - 1
          values = tf.strings.to_number(feature_split.values[1::2], out_type=dtype)
          feature_indices = tf.stack([tf.range(tf.size(indices)) // tf.size(features), indices], axis=1)
          feature_shape = tf.stack([tf.size(input), num_features])
          return labels, feature_indices, values, feature_shape