Thanks for your reply. I think I’ve failed to communicate that the extra float is not the label. The image dataset is split into a few distinct categories and each image will have a positive float associated with it. So my dataset is something like this:
class CustomImageDataset(ImageFolder):
    def __getitem__(self, index):
        image, labels = super().__getitem__(index)
        # logic here to obtain the float here from csv file
        extra_float = torch.Tensor([extra_float])
        return image, extra_float, labels

The question is how to correctly use extra_float in the forward method during training so that it improves classification accuracy of objects split into a few categories.