# Convert data to CUDA tensors
X_gpu = torch.tensor(X, device='cuda:0')
y_gpu = torch.tensor(y, device='cuda:0')

# Create DataLoader
train_dataset = TensorDataset(X_gpu, y_gpu)
train_loader = DataLoader(train_dataset, batch_size=32) # Adjust batch size as needed

# Define base estimators with updated and more advanced models
base_estimators = [
('rf', RandomForestRegressor(
random_state=42,
n_jobs=4, # Limit CPU usage
max_depth=25,
n_estimators=200,
min_samples_leaf=2,
min_samples_split=4
)),
('hist_gb', HistGradientBoostingRegressor(
random_state=42,
max_iter=200,
learning_rate=0.05,
max_depth=8,
min_samples_leaf=20,
max_bins=255
)),
('xgb', XGBRegressor(
objective='reg:squarederror',
random_state=42,
tree_method='hist',
device='cuda',
sampling_method='gradient_based',
grow_policy='lossguide',
max_depth=8,
subsample=0.8,
colsample_bytree=0.8,
learning_rate=0.05,
n_estimators=200,
min_child_weight=4,
gamma=0.2
)),
('catboost', CatBoostRegressor(
random_seed=42,
verbose=0,
depth=8,
learning_rate=0.05,
n_estimators=200,
thread_count=4,
subsample=0.8,
rsm=0.8,
train_dir=custom_tmp_dir
))
]

# Updated parameter space for BayesSearchCV focusing on relevant parameters
param_spaces = {
# RandomForestRegressor parameters
'stack__rf__n_estimators': Integer(150, 250),
'stack__rf__max_depth': Integer(15, 25),
'stack__rf__min_samples_split': Integer(4, 8),
'stack__rf__min_samples_leaf': Integer(2, 4),

# HistGradientBoostingRegressor parameters
'stack__hist_gb__max_iter': Integer(150, 250),
'stack__hist_gb__learning_rate': Real(0.03, 0.08, prior='log-uniform'),
'stack__hist_gb__max_depth': Integer(6, 10),
'stack__hist_gb__min_samples_leaf': Integer(10, 30),
'stack__hist_gb__max_bins': Integer(2, 255),

# XGBRegressor parameters
'stack__xgb__n_estimators': Integer(150, 250),
'stack__xgb__learning_rate': Real(0.03, 0.08, prior='log-uniform'),
'stack__xgb__max_depth': Integer(6, 10),
'stack__xgb__subsample': Real(0.7, 0.9, prior='uniform'),
'stack__xgb__colsample_bytree': Real(0.7, 0.9, prior='uniform'),
'stack__xgb__min_child_weight': Integer(3, 5),
'stack__xgb__gamma': Real(0.1, 0.3, prior='uniform'),

# CatBoostRegressor parameters
'stack__catboost__depth': Integer(6, 10),
'stack__catboost__learning_rate': Real(0.03, 0.08, prior='log-uniform'),
'stack__catboost__n_estimators': Integer(150, 250),
'stack__catboost__subsample': Real(0.7, 0.9, prior='uniform'),
'stack__catboost__rsm': Real(0.7, 0.9, prior='uniform')
}

# Define meta-model
meta_model = RidgeCV(
alphas=np.logspace(-3, 3, 7),
scoring='r2',
store_cv_results=True
)

# Define the Stacking Regressor with advanced models
stacking_regressor = StackingRegressor(
estimators=base_estimators,
final_estimator=meta_model,
passthrough=False,
n_jobs=4, # Limited for memory efficiency
cv=3 # Reduced folds for memory efficiency in final estimator
)

# Create a pipeline with StandardScaler and Stacking Regressor
pipeline = Pipeline([
('scaler', StandardScaler()), # For linear meta-model (Ridge)
('stack', stacking_regressor)
])

# Define BayesSearchCV for advanced hyperparameter optimization
bayes_search = BayesSearchCV(
estimator=pipeline,
search_spaces=param_spaces,
n_iter=40, # Number of iterations for the search
cv=group_kfold.split(X, y, groups=groups),
scoring='r2',
n_jobs=4, # Limit for stability and memory usage
verbose=1,
random_state=42,
pre_dispatch='2*n_jobs', # Memory-efficient dispatch
error_score='raise'
)

# Fit BayesSearchCV with sample weights
fit_params = {'stack__sample_weight': sample_weights} if sample_weights is not None else {}
bayes_search.fit(X, y, **fit_params)
print("BayesSearchCV completed with Spatial GroupKFold and Feature Scaling.")

# Retrieve the best model
best_pipeline = bayes_search.best_estimator_
print(f"Best parameters found: {bayes_search.best_params_}")

Is that's the correct implementation of cuda dataloader from pytorch for xgboost regressor right there? Anything to tweak?