code: def setup_dataloaders(
      train_dataset_path: components.InputPath("tensor"),  # noqa: F821
      test_dataset_path: components.InputPath("tensor"),  # noqa: F821
      train_dataloader_path: components.OutputPath(
          "dataloader"
      ),  # noqa: F821
      test_dataloader_path: components.OutputPath(
          "dataloader"
      ),  # noqa: F821
      batch_size: int = 32,
  ):
      import torch
  
      train_dataset = torch.load(train_dataset_path)
      test_dataset = torch.load(test_dataset_path)
  
      train_loader = torch.utils.data.DataLoader(
          train_dataset,
          batch_size=batch_size,
      )
      test_loader = torch.utils.data.DataLoader(
          test_dataset,
          batch_size=batch_size,
      )
  
      torch.save(train_loader, train_dataloader_path)
      torch.save(test_loader, test_dataloader_path)
  
file_path: wandb/tests/functional_tests/t0_main/kfp/kfp-pipeline-pytorch.py
imports: import os
  import random
  import kfp
  import kfp.dsl
  import wandb
  from kfp import components
  from kubernetes.client.models import V1EnvVar
  from wandb.integration.kfp import wandb_log
  from wandb_probe import wandb_probe_package
label_desc: Use image node_id 3 for calling a global function with example usage: setup_dataloaders(train_dataset_path, test_dataset_path, train_dataloader_path, test_dataloader_path, batch_size) without return types