code: def _data_loader(is_train, batch_size):
      return torch.utils.data.DataLoader(
          datasets.CIFAR10(
              str(lab.get_data_path()),
              train=is_train,
              download=True,
              transform=transforms.Compose(
                  [
                      transforms.ToTensor(),
                      transforms.Normalize(
                          (0.5, 0.5, 0.5), (0.5, 0.5, 0.5)
                      ),
                  ]
              ),
          ),
          batch_size=batch_size,
          shuffle=True,
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  import torch.utils.data
  from torchvision import datasets, transforms
  from labml import experiment, lab, tracker, logger
  from labml.configs import BaseConfigs, option
  from labml.internal.configs.dynamic_hyperparam import FloatDynamicHyperParam, IntDynamicHyperParam
label_desc: Use image node_id 1 for calling a global function with example usage: _data_loader(is_train, batch_size) and returns: torch