v.SetComplex(s.scanComplex(verb, v.Type().Bits()))
		default:
			s.errorString("can't scan type: " + val.Type().String())
		}
	}
}

// errorHandler turns local panics into error returns.
func errorHandler(errp *error) {
	if e := recover(); e != nil {
		if se, ok := e.(scanError); ok { // catch local error
			*errp = se.err

	// proxying the request to another node.
	for k := range w.Header() {
		w.Header().Del(k)
	}

	f := handlers.NewForwarder(&handlers.Forwarder{
		PassHost:     true,
		RoundTripper: ep.Transport,
		ErrorHandler: func(w http.ResponseWriter, r *http.Request, err error) {
			success = false
			if err != nil && !errors.Is(err, context.Canceled) {
				replLogIf(GlobalContext, err)
			}
		},
	})

		// the custom responder might be used for providing a unified error reporting
		// or supporting retry mechanisms by not sending non-fatal errors to the clients
		proxy.ErrorHandler = h.Responder.Error
	}
	proxy.ServeHTTP(w, newReq)
}

type noSuppressPanicError struct{}

func (noSuppressPanicError) Write(p []byte) (n int, err error) {

}

// Creates a new filesystem watcher and adds watches for the config file.
func (o *Options) initWatcher() error {
	fswatcher := filesystem.NewFsnotifyWatcher()
	err := fswatcher.Init(o.eventHandler, o.errorHandler)
	if err != nil {
		return err
	}
	err = fswatcher.AddWatch(o.ConfigFile)
	if err != nil {
		return err
	}
	o.watcher = fswatcher
	return nil
}

  mlir::StatusScopedDiagnosticHandler error_handler(module.getContext());
  mlir::LogicalResult result = mlir::TF::InferShapeForFunction(
      main_func, arg_shapes_copy, producer_version);

  if (failed(result)) {
    return error_handler.Combine(
        errors::Internal("MLIR Shape refinement failed"));
  }
  return error_handler.ConsumeStatus();
}

    return tensorflow::errors::Aborted(
        "Failed to apply MLIR pass manager CL options.");
  }

  auto error_handler = [&](const Twine& msg) {
    emitError(UnknownLoc::get(pm.getContext())) << msg;
    return failure();
  };
  if (failed(pass_pipeline.addToPipeline(pm, error_handler))) {
    return tensorflow::errors::Aborted("Failed to add passes to pipeline.");
  }