tensorflow::protobuf::TextFormat::PrintToString(actual, &actual_text_proto);
  EXPECT_EQ(expected_text_proto, actual_text_proto);

  const string malformed_text_proto(R"(cluster {
  job {
    name: "worker")");
  TF_Buffer* null_result =
      TFE_GetServerDef(malformed_text_proto.c_str(), status);
  EXPECT_NE(TF_GetCode(status), TF_OK);
  EXPECT_TRUE(absl::StrContains(TF_Message(status),

    const auto tensor_interface =
        tensorflow::down_cast<const tensorflow::TensorInterface*>(src->tensor);

    if (dst->dims() != 0) {
      return InvalidArgument(
          "Malformed TF_RESOURCE tensor: expected a scalar, got a tensor with "
          "shape ",
          dst->shape().DebugString());
    }
    *dst = tensorflow::Tensor(tensorflow::DT_RESOURCE, dst->shape());

  ///     existing file or one of the parent entries in `path` doesn't exist.
  ///   * Must set `status` to `TF_FAILED_PRECONDITION` if `path` points to a
  ///     directory or if it is invalid (e.g., malformed, or has a parent entry
  ///     which is a file).
  ///   * Might use any other error value for `status` to signal other errors.
  ///
  /// REQUIREMENTS: If plugins implement this, they must also provide a filled

*   `tf.function`:

    *   `tf.function` now uses the Python inspect library directly for parsing the signature of the Python function it is decorated on. This change may break code where the function signature is malformed, but was ignored previously, such as:
        *   Using `functools.wraps` on a function with different signature
        *   Using `functools.partial` with an invalid `tf.function` input