First off, what do you mean by “I want to append/add another tensor to the trainloader (e.g., weights for training data)”.  That sounds like you want to load previously trained weights into your model.  This has nothing to do with the dataloader or dataset.  Is this what you are trying to do? 22.
You can add the transformations to any dataset.  I think appending items to this dataset will be quite difficult, because the dataset itself seems to be a bunch of pickle files.  So you’d have to add items to the pickle to make this process repeatable.  Going through the process of creating your own dataset based on the official CIFAR10 one would probably be helpful since you are doing more than just using cifar.
Having said that…
you could try to directly access dataset and append your images as numpy arrays and labels as ints by accessing
newimg, newlabel = np.random.rand(1, 32, 32, 3), 1
ds = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)
dl = torch.utils.data.DataLoader(ds, batch_size=4,
                                          shuffle=True, num_workers=2)
ds.data = np.r_[dl.dataset.data, newimg]
dl.targets.append(newlabel)

That seems more difficult.  Also, it is sometimes the case that the sampler doesn’t pick up these changes.  I think you could access that directly, but again, you should just rewrite the Dataset.  You can start with the official code and work from there.
https://pytorch.org/docs/stable/_modules/torchvision/datasets/cifar.html#CIFAR10 70