Thanks for the reply, ptrblck.
I did try something like
def segmentation_transform(image, tar, train=False):
    image_cc = TF.center_crop(image, 1600)
    tar_cc = TF.center_crop(tar, 1600)
    image_T = TF.to_tensor(image_cc)
    tar_T = TF.to_tensor(tar_cc)
    if train:
        image_T = TF.hflip(image_T)
        tar_T = TF.hflip(tar_T)
    return image_T,tar_T

I’m not sure if that is what you meant. I got similar positional argument error with that when I run
dataset = four_chs(root = '/content/drive/MyDrive/data', transforms = get_transform(train=True)) 
data_loader = torch.utils.data.DataLoader(
 dataset, batch_size=1, shuffle=True, num_workers=2,
 collate_fn=utils.collate_fn)

# For Training
images,targets = next(iter(data_loader))

Unfortunately, it is still not clear to me how I can modify either the transforms function, so that I can use a T.Resize() with a size argument or a T.CenterCrop() with a size argument inside of the function and avoid getting the positional argument error (or maybe both transforms inside the function), or write a new class of Compose that can execute the transforms with the Dataset I create and read in with the Dataloader.