如何在xgboost的github存储库的自定义目标函数示例脚本中计算对数损失的梯度和hessian？

import numpy as np
import xgboost as xgb

def log_loss_obj(preds, dtrain):
    labels = dtrain.get_label()
    preds = 1.0 / (1.0 + np.exp(-preds))  # 将预测值转换为概率
    grad = preds - labels  # 计算梯度
    hess = preds * (1.0 - preds)  # 计算Hessian
    return grad, hess

data = np.random.rand(100, 10)
labels = np.random.randint(2, size=100)
dtrain = xgb.DMatrix(data, label=labels)

params = {'objective': log_loss_obj, 'eval_metric': 'logloss'}
model = xgb.train(params, dtrain, num_boost_round=10)