code: def build_data_loaders(batch_size, split_seed=1):
      normalize = [
          transforms.ToTensor(),
          transforms.Normalize(
              (0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)
          ),
      ]
  
      augment = [
          transforms.RandomCrop(32, padding=4),
          transforms.RandomHorizontalFlip(),
      ]
  
      train_set = torchvision.datasets.CIFAR10(
          root="./data",
          train=True,
          download=True,
          transform=transforms.Compose(augment + normalize),
      )
      valid_set = torchvision.datasets.CIFAR10(
          root="./data",
          train=True,
          download=True,
          transform=transforms.Compose(normalize),
      )
      test_set = torchvision.datasets.CIFAR10(
          root="./data",
          train=False,
          download=True,
          transform=transforms.Compose(normalize),
      )
  
      num_train = 45000
      # num_valid = 5000
      indices = numpy.arange(num_train)
      numpy.random.RandomState(split_seed).shuffle(indices)
  
      train_idx, valid_idx = indices[:num_train], indices[num_train:]
      train_sampler = SubsetRandomSampler(train_idx)
      valid_sampler = SubsetRandomSampler(valid_idx)
  
      train_loader = torch.utils.data.DataLoader(
          train_set,
          batch_size=batch_size,
          sampler=train_sampler,
          num_workers=5,
      )
      valid_loader = torch.utils.data.DataLoader(
          train_set,
          batch_size=1000,
          sampler=train_sampler,
          num_workers=5,
      )
      test_loader = torch.utils.data.DataLoader(
          test_set, batch_size=1000, shuffle=False, num_workers=5
      )
  
      return train_loader, valid_loader, test_loader
  
file_path: orion/examples/tutorials/code_2_hyperband_checkpoint.py
imports: import os
  import numpy
  import torch
  import torch.nn
  import torch.optim
  import torchvision
  import torchvision.models
  import torchvision.transforms
  from torch.utils.data import SubsetRandomSampler
  from orion.client import build_experiment
label_desc: Use image node_id 1 for calling a global function with example usage: build_data_loaders(batch_size, split_seed) and returns: train_loader,  valid_loader,  test_loader