I have composed some transforms for my training images in the following way:
data_transform = transforms.Compose([
    transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.2),
    transforms.FiveCrop((224, 224)),
    transforms.RandomAffine(degrees=15, translate=(0.05, 0.05), scale=(0.9, 1.1),
                            shear=0.1),
    transforms.RandomPerspective(distortion_scale=0.5, p=0.5, interpolation=2,
                                 fill=0),
])

Now it needs to be provided to torchvision.datasets.ImageFolder or some other ways exist which I am not aware of yet.
My dataset is originally in pandas dataframe, I split it for 5-fold cross validation and normalize the data in two different ways, because the last column of the dataframe is actually those image arrays and the rest is numerical data.
I am using train = torch.utils.data.TensorDataset(x_train_fold, x_train_NUM_fold, y_train_fold) where x_train_fold is a tensor of training set.
How do I insert the transforms so that the model receives them while training?