input: Write a function to decode LibSVM input to tensors using Python wrappers around TensorFlow ops. The function `decode_libsvm` 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 returns 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 1:1 2:2", "2 3:3 4:4"], 6, label_dtype=tf.int32) == (Tensor([1, 2], dtype=tf.int32), Tensor([[0, 1], [0, 2], [1, 3], [1, 4]], dtype=tf.int64), Tensor([1, 2, 3, 4], dtype=tf.float32), Tensor([2, 4], dtype=tf.int64))
  assert decode_libsvm(["1 1:1 2:2", "2 3:3 4:4"], 6, dtype=tf.float64, label_dtype=tf.float32) == (Tensor([1, 2], dtype=tf.float32), Tensor([[0, 1], [0, 2], [1, 3], [1, 4]], dtype=tf.int64), Tensor([1, 2, 3, 4], dtype=tf.float64), Tensor([2, 4], dtype=tf.int64))
  assert decode_libsvm([], 6) == (Tensor([], dtype=tf.int64), Tensor([], dtype=tf.int64), Tensor([], dtype=tf.float32), Tensor([0, 6], dtype=tf.int64))
  assert decode_libsvm(["1 1:1 2:2"], 5, dtype=tf.int32, label_dtype=tf.int64) == (Tensor([1], dtype=tf.int64), Tensor([[0, 1], [0, 2]], dtype=tf.int64), Tensor([1, 2], dtype=tf.int32), Tensor([1, 5], dtype=tf.int64))
  assert decode_libsvm(["1 1:1 2:2"], 5, dtype=tf.int64, label_dtype=tf.int32) == (Tensor([1], dtype=tf.int32), Tensor([[0, 1], [0, 2]], dtype=tf.int64), Tensor([1, 2], dtype=tf.int64), Tensor([1, 5], dtype=tf.int64))
output: 
  def decode_libsvm(input, num_features, dtype=tf.float32, label_dtype=tf.int64, name=None):
      with tf.name_scope(name, "decode_libsvm", [input, num_features, dtype, label_dtype]):
          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(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_line, input, dtype=(label_dtype, tf.int64, dtype),
              infer_shape=False, name="map_parse_line"
          )
          
          labels = tf.stack(labels)
          indices = tf.reshape(tf.ragged.stack(indices_list).flat_values, [-1, 1])
          values = tf.reshape(tf.ragged.stack(values_list).flat_values, [-1])
          feature_indices = tf.concat([
              tf.expand_dims(tf.range(tf.shape(indices)[0], dtype=tf.int64), 1),
              indices
          ], axis=1)
          feature_shape = tf.stack([tf.shape(labels)[0], num_features])
          
          return labels, feature_indices, values, feature_shape
  