def METHOD_NAME(data_path):
    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                     std=[0.229, 0.224, 0.225])
    dataset = torchvision.datasets.ImageNet(data_path,
                                            split="train",
                                            transform=transforms.Compose([transforms.RandomResizedCrop(224),
                                                                          transforms.RandomHorizontalFlip(),
                                                                          transforms.ToTensor(),
                                                                          normalize]))
    dataset_test = torchvision.datasets.ImageNet(data_path,
                                                 split="val",
                                                 transform=transforms.Compose([transforms.Resize(256),
                                                                               transforms.CenterCrop(224),
                                                                               transforms.ToTensor(),
                                                                               normalize]))
    train_sampler = torch.utils.data.RandomSampler(dataset)
    test_sampler = torch.utils.data.SequentialSampler(dataset_test)
    data_loader = torch.utils.data.DataLoader(
        dataset, batch_size=train_batch_size,
        sampler=train_sampler)
    data_loader_test = torch.utils.data.DataLoader(
        dataset_test, batch_size=eval_batch_size,
        sampler=test_sampler)
    return data_loader, data_loader_test