diff --git a/PyTorch/Forecasting/TFT/criterions.py b/PyTorch/Forecasting/TFT/criterions.py
index 12de5be76..566467a23 100644
--- a/PyTorch/Forecasting/TFT/criterions.py
+++ b/PyTorch/Forecasting/TFT/criterions.py
@@ -29,6 +29,10 @@ def forward(self, predictions, targets):
         return losses
 
 def qrisk(pred, tgt, quantiles):
+    if isinstance(pred, torch.Tensor):
+        pred = pred.detach().cpu().numpy()
+    if isinstance(tgt, torch.Tensor):
+        tgt = tgt.detach().cpu().numpy()
     diff = pred - tgt
     ql = (1-quantiles)*np.clip(diff,0, float('inf')) + quantiles*np.clip(-diff,0, float('inf'))
     losses = ql.reshape(-1, ql.shape[-1])
diff --git a/PyTorch/Forecasting/TFT/inference.py b/PyTorch/Forecasting/TFT/inference.py
index 7f60f5588..cbbffe4e4 100644
--- a/PyTorch/Forecasting/TFT/inference.py
+++ b/PyTorch/Forecasting/TFT/inference.py
@@ -111,7 +111,8 @@ def predict(args, config, model, data_loader, scalers, cat_encodings, extend_tar
 
 def visualize_v2(args, config, model, data_loader, scalers, cat_encodings):
     unscaled_predictions, unscaled_targets, ids, _ = predict(args, config, model, data_loader, scalers, cat_encodings, extend_targets=True)
-
+    unscaled_predictions = torch.tensor(unscaled_predictions)
+    unscaled_targets = torch.tensor(unscaled_targets)
     num_horizons = config.example_length - config.encoder_length + 1
     pad = unscaled_predictions.new_full((unscaled_targets.shape[0], unscaled_targets.shape[1] - unscaled_predictions.shape[1], unscaled_predictions.shape[2]), fill_value=float('nan'))
     pad[:,-1,:] = unscaled_targets[:,-num_horizons,:]
@@ -138,6 +139,8 @@ def inference(args, config, model, data_loader, scalers, cat_encodings):
     if args.joint_visualization or args.save_predictions:
         ids = torch.from_numpy(ids.squeeze())
         #ids = torch.cat([x['id'][0] for x in data_loader.dataset])
+        unscaled_predictions = torch.tensor(unscaled_predictions)
+        unscaled_targets = torch.tensor(unscaled_targets)
         joint_graphs = torch.cat([unscaled_targets, unscaled_predictions], dim=2)
         graphs = {i:joint_graphs[ids == i, :, :] for i in set(ids.tolist())}
         for key, g in graphs.items(): #timeseries id, joint targets and predictions