input: Write a function to decode LibSVM input to tensors using TensorFlow ops. The function should be named `decode_libsvm` and 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 2:3 4:5"]), 2, dtype=tf.float32, label_dtype=tf.int32)==(tf.constant([1]), tf.constant([[1, 0], [1, 1]]), tf.constant([3.0, 5.0]), tf.constant([2]))
  assert decode_libsvm(tf.constant(["1 2:3 4:5"]), 5, dtype=tf.float64, label_dtype=tf.int64)==(tf.constant([1]), tf.constant([[1, 1], [1, 3]]), tf.constant([3.0, 5.0]), tf.constant([2]))
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([0, tf.strings.to_number(idx, out_type=tf.int64) - 1])
              values.append(tf.strings.to_number(val, out_type=dtype))
          return label, tf.stack(indices), tf.stack(values), tf.constant([num_features], dtype=tf.int64)
  
      labels, indices, values, shapes = tf.map_fn(
          parse_line, input,
          fn_output_signature=(label_dtype, tf.int64, dtype, tf.int64)
      )
      return labels, indices, values, shapes