for i, data in enumerate(all_dataloader):
        train_full, w_full, train_x_1, w_x_1, train_x_2, w_x_2 = data
        train_full = train_full.to(device)
        w_full = w_full.to(device)
        train_x_1 = train_x_1.to(device)
        w_x_1 = w_x_1.to(device)
        train_x_2 = train_x_2.to(device)
        w_x_2 = w_x_2.to(device)
        
        noise_1 = torch.empty(train_x_1.shape[0], LATENT_DIMENSIONS, device=device).normal_(mean=0,std=1.0)
        noise_2 = torch.empty(train_x_2.shape[0], LATENT_DIMENSIONS, device=device).normal_(mean=0,std=1.0)
        generated_y_1 = netG(torch.cat((noise_1, train_x_1), dim=1))
        generated_full_1 = torch.cat((generated_y_1, train_x_1), dim=1)
        generated_y_2 = netG(torch.cat((noise_2, train_x_2), dim=1))
        generated_full_2 = torch.cat((generated_y_2, train_x_2), dim=1)

all of them are just tensors from Dataset class and define like:
    def __init__(self, dataset, batch_size):
        self.dataset = torch.Tensor(dataset)

all_data = torch.utils.data.TensorDataset(train_full_.dataset, w_full_.dataset, train_x_1_.dataset, 
                                          w_x_1_.dataset, train_x_2_.dataset, w_x_2_.dataset)
all_dataloader = DataLoader(all_data, batch_size=BATCH_SIZE, shuffle=True)

that’s all about them, no any other computation