Dice Object Initialization Error

[ntheol]

Hello!

I have trained an ML pipeline for a pytorch model by using the skorch wrapper like this:

nn = NeuralNetBinaryClassifier(nn_model,criterion=nn.BCELoss(),optimizer=torch.optim.AdamW, max_epochs=10, batch_size=32,verbose=False)

numerical_transformer = Pipeline([
('scaler', StandardScaler())
])

label_encoded_transformer = Pipeline([
('label_encoder', OrdinalEncoder())
])

one_hot_encoded_transformer = Pipeline([
('one_hot_encoder', OneHotEncoder())
])

preprocessor = ColumnTransformer(
transformers=[
('num', numerical_transformer,numeric_columns),
# ('label',label_encoded_transformer,label_encoded_features),
('one_hot', one_hot_encoded_transformer, cat_columns)
])

params = {
"Model__lr": [0.001, 0.01, 0.1],
"Model__batch_size": (16,64), # Different batch sizes
"Model__optimizer": [AdamW, Adam,SGD], # Different optimizers
"preprocessor__num__scaler": [StandardScaler(), MinMaxScaler(),RobustScaler()]
}

ml_pipeline = Pipeline([("preprocessor", preprocessor),
('float32', FunctionTransformer(func=convert_to_float32)),
("Model", nn)])

optimizer = GridSearchCV(ml_pipeline,param_grid=params,optimization_algorithm='grid_search')
opt = optimizer.fit(train,train_labels)

Does DiCE support this kind of model?
When trying to initialize it in this way :
d = dice_ml.Data(dataframe=train, continuous_features=numeric_cols.tolist(), outcome_name='label')

m = dice_ml.Model(model=opt.best_estimator_, backend="sklearn")

#Initializing DiCE object
exp = dice_ml.Dice(d, m, method="random")

I get the below error:

Is it because DiCE does not support the skorch wrapper?
Thank you!