tform = transforms.Compose([
transforms.ToPILImage(), # Must convert to PIL image for subsequent operations to run
transforms.RandomRotation(20), # Image augmentation
transforms.ToTensor(), # Must convert to pytorch tensor for subsequent operations to run
transforms.Lambda(lambda x: x.repeat(int(3/x.shape[0]), 1, 1)), # Some images are grayscale, so we need to add channels
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
dataloader = ds.pytorch(num_workers=0, batch_size=4, transform = {'images': tform, 'labels': None}, shuffle = True)