if (fields.isEmpty() && extraQueries.length == 0) {
                result = ComponentUtil.getChatClient().chat(sessionId, message, userId);
            } else {
                result = ComponentUtil.getChatClient().chat(sessionId, message, userId, fields, extraQueries);
            }

            if (logger.isDebugEnabled()) {

            e.setId(userCode);
            e.setCreatedAt(now);
            e.setUpdatedAt(now);
            return e;
        });
        CommonPoolUtil.execute(() -> userInfoBhv.insertOrUpdate(userInfo));
        return userInfo;
    }

    /**
     * Gets user information.
     *
     * @param userCode The user code.
     * @return The user information.
     */

        return new SearchLogHelper.SearchLogContext((FessConfig) ComponentUtil.getFessConfig(), new String[0], // roles
                null, // userCode
                null, // userId
                request, // request
                "127.0.0.1", // clientIp
                "" // virtualHostKey
        );
    }

    # A local firewall rule for the container is added in
    # ci/official/utilities/setup_docker.sh.
  fi
else
  # The volume mapping flag below shares the user's gcloud credentials, if any,
  # with the container, in case the user has credentials stored there.
  # This would allow Bazel to authenticate for RBE.
  # Note: TF's CI does not have any credentials stored there.

```Python
def get_current_user(token: str):
    # authenticate user
    return User()


@app.get("/items/")
def read_items(user: User = Depends(get_current_user)):
    ...


@app.post("/items/")
def create_item(*, user: User = Depends(get_current_user), item: Item):
    ...


@app.get("/items/{item_id}")
def read_item(*, user: User = Depends(get_current_user), item_id: int):
    ...

    *   New `tf.data.Dataset.reduce()` API allows users to reduce a finite
        dataset to a single element using a user-provided reduce function.
    *   New `tf.data.Dataset.window()` API allows users to create finite windows
        of input dataset; when combined with the `tf.data.Dataset.reduce()` API,
        this allows users to implement customized batching.