diff --git a/intermediate_source/torchvision_tutorial.rst b/intermediate_source/torchvision_tutorial.rst
index 7e700f9bd1b..c82b8097e93 100644
--- a/intermediate_source/torchvision_tutorial.rst
+++ b/intermediate_source/torchvision_tutorial.rst
@@ -327,6 +327,30 @@ transformation:
            transforms.append(T.RandomHorizontalFlip(0.5))
        return T.Compose(transforms)
 
+
+Testing ``forward()`` method (Optional)
+---------------------------------------
+
+Before iterating over the dataset, it's good to see what the model 
+expects during training and inference time on sample data.
+
+.. code:: python
+
+   model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
+   dataset = PennFudanDataset('PennFudanPed', get_transform(train=True))
+   data_loader = torch.utils.data.DataLoader(
+    dataset, batch_size=2, shuffle=True, num_workers=4,
+    collate_fn=utils.collate_fn)
+   # For Training
+   images,targets = next(iter(data_loader))
+   images = list(image for image in images)
+   targets = [{k: v for k, v in t.items()} for t in targets]
+   output = model(images,targets)   # Returns losses and detections
+   # For inference
+   model.eval()
+   x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]
+   predictions = model(x)           # Returns predictions 
+
 Let’s now write the main function which performs the training and the
 validation: