diff --git a/hummingbird/ml/containers/_sklearn_api_containers.py b/hummingbird/ml/containers/_sklearn_api_containers.py
index 16dc63a73..6eecca12e 100644
--- a/hummingbird/ml/containers/_sklearn_api_containers.py
+++ b/hummingbird/ml/containers/_sklearn_api_containers.py
@@ -64,7 +64,7 @@ def _run(self, function, *inputs):
                 splits = [inputs[input_names[idx]] for idx in range(len(input_names))]
                 inputs = [df.to_numpy().reshape(-1, 1) for df in splits]
 
-        return function(*inputs)
+        return function(inputs)
 
 
 class SklearnContainerTransformer(SklearnContainer):
diff --git a/hummingbird/ml/containers/sklearn/pytorch_containers.py b/hummingbird/ml/containers/sklearn/pytorch_containers.py
index 7745e5681..4b66c0b04 100644
--- a/hummingbird/ml/containers/sklearn/pytorch_containers.py
+++ b/hummingbird/ml/containers/sklearn/pytorch_containers.py
@@ -186,8 +186,6 @@ def _predict(self, *inputs):
                 return output
             else:
                 return output.ravel()
-        elif self._is_anomaly_detection:
-            return self.model.forward(*inputs)[0].cpu().numpy().ravel()
         else:
             return self.model.forward(*inputs)[0].cpu().numpy().ravel()
 
@@ -282,7 +280,7 @@ class TorchScriptSklearnContainerClassification(PyTorchSklearnContainerClassific
     def predict(self, *inputs):
         device = get_device(self.model)
         f = super(TorchScriptSklearnContainerClassification, self)._predict
-        f_wrapped = lambda x: _torchscript_wrapper(device, f, x, extra_config=self._extra_config)  # noqa: E731
+        f_wrapped = lambda x: _torchscript_wrapper(device, f, *x, extra_config=self._extra_config)  # noqa: E731
 
         return self._run(f_wrapped, *inputs)