input: Write a function to decode LibSVM input to tensors using TensorFlow. The function should be named `decode_libsvm` and 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(tf.constant(["1 2:1.5", "2 3:2.0"]), 3)[0].numpy() == [1, 2]
  assert decode_libsvm(tf.constant(["1 2:1.5", "2 3:2.0"]), 3)[1].numpy() == [[0, 1], [1, 2]]
  assert decode_libsvm(tf.constant(["1 2:1.5", "2 3:2.0"]), 3)[2].numpy() == [1.5, 2.0]
  assert decode_libsvm(tf.constant(["1 2:1.5", "2 3:2.0"]), 3)[3].numpy() == [2, 3]
  assert decode_libsvm(tf.constant([]), 3)[0].numpy() == []
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, value = tf.strings.split([token], ':')
              indices.append(tf.strings.to_number(idx, out_type=tf.int64))
              values.append(tf.strings.to_number(value, out_type=dtype))
          return label, indices, values
  
      labels, indices, values = tf.map_fn(
          parse_libsvm,
          input,
          dtype=(label_dtype, tf.int64, dtype),
          name=name
      )
  
      labels = tf.stack(labels)
      indices = tf.ragged.stack(indices).flat_values
      values = tf.ragged.stack(values).flat_values
      feature_shape = tf.constant([tf.shape(input)[0], num_features], dtype=tf.int64)
  
      return labels, indices, values, feature_shape