code: def prepare_loaders(conf, use_cuda=False):
      kwargs = (
          {"num_workers": 1, "pin_memory": True} if use_cuda else {}
      )
      train_loader = torch.utils.data.DataLoader(
          datasets.MNIST(
              "../data",
              train=True,
              download=True,
              transform=transforms.Compose(
                  [
                      transforms.ToTensor(),
                      transforms.Normalize((0.1307,), (0.3081,)),
                  ]
              ),
          ),
          batch_size=conf.batch_size,
          shuffle=True,
          **kwargs,
      )
  
      test_loader = torch.utils.data.DataLoader(
          datasets.MNIST(
              "../data",
              train=False,
              transform=transforms.Compose(
                  [
                      transforms.ToTensor(),
                      transforms.Normalize((0.1307,), (0.3081,)),
                  ]
              ),
          ),
          batch_size=conf.test_batch_size,
          shuffle=True,
          **kwargs,
      )
      return train_loader, test_loader
  
file_path: pytorch-optimizer/examples/mnist.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.optim.lr_scheduler import StepLR
  from torch.utils.tensorboard import SummaryWriter
  from torchvision import datasets, transforms, utils
  import torch_optimizer
label_desc: Use image node_id 3 for calling a global function with example usage: prepare_loaders(conf, use_cuda) and returns: train_loader,  test_loader