code: def main():
      device = "cuda" if torch.cuda.is_available() else "cpu"
      print(f'Running on device "{device}"')
  
      mtcnn = MTCNN(device=device)
  
      batch_size = 32
  
      # Generate data loader
      ds = datasets.ImageFolder(
          root="data/test_images/",
          transform=transforms.Resize((512, 512)),
      )
      dl = DataLoader(
          dataset=ds,
          num_workers=4,
          collate_fn=training.collate_pil,
          batch_size=batch_size,
          sampler=RandomSampler(ds, replacement=True, num_samples=960),
      )
  
      start = time.time()
      faces = []
      for x, _ in tqdm(dl):
          faces.extend(mtcnn(x))
      elapsed = time.time() - start
      print(
          f"Elapsed: {elapsed} | EPS: {len(dl) * batch_size / elapsed}"
      )
  
imports: from facenet_pytorch import MTCNN, training
  import torch
  from torchvision import datasets, transforms
  from torch.utils.data import DataLoader, RandomSampler
  from tqdm import tqdm
  import time
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types