How to train LGBMClassifier using optuna

I am trying to use lgbm with optuna for a classification task.

Here is my model.

from optuna.integration import LightGBMPruningCallback
import optuna.integration.lightgbm as lgbm
import optuna

def objective(trial, X_train, y_train, X_test, y_test):
    param_grid = {
        # device_type: trial.suggest_categorical(device_type, ['gpu']),
        n_estimators: trial.suggest_categorical(n_estimators, [10000]),
        learning_rate: trial.suggest_float(learning_rate, 0.01, 0.3, log=True),
        num_leaves: trial.suggest_int(num_leaves, 20, 3000, step=20),
        max_depth: trial.suggest_int(max_depth, 3, 12), 
        min_data_in_leaf: trial.suggest_int(min_data_in_leaf, 100, 10000, step=1000),
        lambda_l1: trial.suggest_int(lambda_l1, 0, 100, step=5),
        min_gain_to_split: trial.suggest_float(min_gain_to_split, 0, 15),
        bagging_fraction: trial.suggest_float(
            bagging_fraction, 0.2, 0.95, step=0.1
        ),
        bagging_freq: trial.suggest_categorical(bagging_freq, [1]),
        feature_fraction: trial.suggest_float(
            feature_fraction, 0.2, 0.95, step=0.1
        ),
        max_features: trial.suggest_categorical(
            max_features, choices=[auto, sqrt, log2]
        ),
        n_jobs: -1,
        random_state: 1121218,
    }
    
    model = lgbm.LGBMClassifier(objective=multiclass, **param_grid)
    model.fit(
        X_train,
        y_train,
        eval_set=[(X_test, y_test)],
        eval_metric=multi_logloss,
        early_stopping_rounds=5,
        callbacks=[
            LightGBMPruningCallback(trial, multi_logloss)
        ],  # Add a pruning callback
    )
    preds = model.predict_proba(X_test)
    return preds, model

I then call the model

%%time
study = optuna.create_study(direction=maximize, study_name=LGBM Classifier)

func = lambda trial: objective(trial, X_train, y_train, X_test, y_test)

preds, model = study.optimize(func, n_trials=100) 

But I get the following error:

RuntimeError: scikit-learn estimators should always specify their parameters in the signature of their __init__ (no varargs). 
class 'optuna.integration._lightgbm_tuner.sklearn.LGBMClassifier' with constructor (self, *args:Any, **kwargs:Any) - None doesn't  follow this convention.

The understand the error, but I'm not sure what the correct way is to do what I want to do.