Thanks for your reply. However I am not able to do what you said in terms of writing a custom Dataset, because to me, the link you shared and my case seems different. Besides, I am not good at organizing a custom class to suit my case.
Let me put in words (and codes), I have a sequence like this:
five_splits = list(StratifiedKFold(n_splits=5, shuffle=True).split(X_numeric_img, y_numeric_img))
where X_numeric_img, y_numeric_img are (1792, 10) and (1792, 1) shape dataframes.
Then I loop through it in the following way:
for i, (train_idx, valid_idx) in enumerate(five_splits):
    X_train_img = np.array([x for x in X_numeric_img[train_idx, -1].flatten()], dtype=float)
    X_test_img = np.array([x for x in X_numeric_img[valid_idx, -1].flatten()], dtype=float)
    X_train_img /= 255
    X_test_img /= 255

    X_train_img_T = np.transpose(X_train_img, (0, 3, 1, 2))  # (1433, 3, 224, 224)
    X_test_img_T = np.transpose(X_test_img, (0, 3, 1, 2))  # (359, 3, 224, 224)

    x_train_fold = torch.tensor(X_train_img_T).to(device, dtype=torch.float)   
    y_train_fold = torch.tensor(y_numeric_img[train_idx]).to(device, dtype=torch.float)

    x_val_fold = torch.tensor(X_test_img_T).to(device, dtype=torch.float)
    y_val_fold = torch.tensor(y_numeric_img[valid_idx]).to(device, dtype=torch.float) 

    std_scaler_train = StandardScaler().fit(X_numeric_img[train_idx, :-1])
    std_scaler_test = StandardScaler().fit(X_numeric_img[valid_idx, :-1])
    X_num_train_normm = std_scaler_train.transform(X_numeric_img[train_idx, :-1]) 
    X_num_test_normm = std_scaler_test.transform(X_numeric_img[valid_idx, :-1])

    x_train_NUM_fold = torch.tensor(X_num_train_normm).to(device, dtype=torch.float)
    x_val_NUM_fold = torch.tensor(X_num_test_normm).to(device, dtype=torch.float)

    train = torch.utils.data.TensorDataset(x_train_fold, x_train_NUM_fold, y_train_fold)
    valid = torch.utils.data.TensorDataset(x_val_fold, x_val_NUM_fold, y_val_fold)

    train_loader = torch.utils.data.DataLoader(train, batch_size=bs, shuffle=True)
    valid_loader = torch.utils.data.DataLoader(valid, batch_size=bs, shuffle=False)

Although a bit lengthy I hope you can see the way I approached it.
After augmenting images (e.g. 5 times), I need to assign numerical data of the original image to those augmented ones too, bacause the netwrok receives two inputs. That part also is making things difficult.
Could you explain it in more detail so that I can use data_transform above?
Thank you once again