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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def train_eval():
 """ train and eval the model """


    global trainloader
    global testloader
    global net

    (x_train, y_train) = trainloader
    (x_test, y_test) = testloader

    # train procedure
    net.fit(
        x=x_train,
        y=y_train,
        batch_size=args.batch_size,
        validation_data=(x_test, y_test),
        epochs=args.epochs,
        shuffle=True,
        callbacks=[
            SendMetrics(),
            EarlyStopping(min_delta=0.001, patience=10),
            TensorBoard(log_dir=TENSORBOARD_DIR),
        ],
    )

    # trial report final acc to tuner
    _, acc = net.evaluate(x_test, y_test)
    logger.debug("Final result is: %.3f", acc)
    nni.report_final_result(acc)