RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn

[IamSparky]

For the loss function I am gtting the error

setting up the training function

loss_fn = CutMixCrossEntropyLoss(True)
if __name__ == "__main__":
    
    set_parameters_requires_grad(model , True)

    epochs = 25

    for epoch in range(epochs):
        print('Epoch ', epoch,'/',epochs-1)
        print('-'*15)

        for phase in ['train', 'val']:
            if phase == 'train':
                model.train()  # Set model to training mode
            else:
                model.eval()   # Set model to evaluate mode

            running_loss = 0.0
            running_corrects = 0.0
            
            # Iterate over data.
            for inputs,labels in dataloaders[phase]:
                if torch.cuda.is_available():
                    inputs = inputs.cuda()
                    labels = labels.cuda()

                # zero the parameter gradients
                optimizer.zero_grad()

                with torch.set_grad_enabled(phase == 'train'):
                    
                    outputs = model(inputs)
                    _, preds = torch.max(outputs, 1)
                    loss = loss_fn(outputs, labels)

                    # we backpropagate to set our learning parameters only in training mode
                    if phase == 'train':
                        loss.backward()
                        optimizer.step()

                running_loss += loss.item() * inputs.size(0)
                running_corrects += torch.sum(preds == torch.argmax(labels)) # (preds == labels.data) as the usage of .data is not recommended, as it might have unwanted side effect.

            # scheduler for weight decay
            if phase == 'train':
                scheduler.step()

            epoch_loss = running_loss / float(dataset_sizes[phase])
            epoch_acc = running_corrects / float(dataset_sizes[phase])

            print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))
    optimizer.swap_swa_sgd()