仿照feature = pd.DataFrame( Parallel(n_jobs=16, backend="multiprocessing")(delayed(processing_feature)(f) for f in tqdm(all_ids)))的方法，使用Parallel和delayed优化下面的代码：mse_weights, ce_weights, soft_weights = nn.fit( train_feature, train_target_lb, gamma, batch_num)

你可以使用`Parallel`和`delayed`来优化下面的代码：

from joblib import Parallel, delayed

def fit_wrapper(train_feature, train_target_lb, gamma, batch_num):
    nn = YourNeuralNetwork()  # 替换为你的神经网络类
    mse_weights, ce_weights, soft_weights = nn.fit(train_feature, train_target_lb, gamma, batch_num)
    return mse_weights, ce_weights, soft_weights

results = Parallel(n_jobs=16, backend="multiprocessing")(
    delayed(fit_wrapper)(train_feature, train_target_lb, gamma, batch_num) 
    for _ in tqdm(range(len(all_ids)))
)

mse_weights_list, ce_weights_list, soft_weights_list = zip(*results)
mse_weights = np.concatenate(mse_weights_list)
ce_weights = np.concatenate(ce_weights_list)
soft_weights = np.concatenate(soft_weights_list)

这里，我们首先定义了一个包装函数`fit_wrapper`，它调用你的神经网络类并返回相应的权重。然后，我们使用`Parallel`和`delayed`来并行化调用`fit_wrapper`函数，这样每个任务可以在不同的CPU核心上同时运行。最后，我们提取各个任务的结果，并将它们连接起来得到最终的权重结果。