bruno-hoermann

Here is my code: ``` import torch.jit import dice_ml from utils.utils import transform_torch_model, create_random_dataset model = transform_torch_model(torch.jit.load("models/model.pt").state_dict()) print(model) df = create_random_dataset(model) df.info() continuous_features = df.drop("action", axis=1).columns.tolist() d = dice_ml.Data(dataframe=df, continuous_features=continuous_features,...

With `posthoch_sparsity_param = None` I receive the following message: ``` No Counterfactuals found for the given configuation, perhaps try with different values of proximity (or diversity) weights or learning rate......

@gaugup, if you could take a look at this it would be much appreciated!

@sunsssk Thank you! Maybe it should be added to the documentation which backend works with multiclass prediction.

@sunsssk However in the Multiclass example the `DiceGenetic` class is used where documentation of `generate_counterfactuals()` says as well that `desired_class` can only take 0 or 1. So I think you...