code: def extract_face(img, box, image_size=160, margin=0, save_path=None):
      """Extract face + margin from PIL Image given bounding box.
  
      Arguments:
          img {PIL.Image} -- A PIL Image.
          box {numpy.ndarray} -- Four-element bounding box.
          image_size {int} -- Output image size in pixels. The image will be square.
          margin {int} -- Margin to add to bounding box, in terms of pixels in the final image.
              Note that the application of the margin differs slightly from the davidsandberg/facenet
              repo, which applies the margin to the original image before resizing, making the margin
              dependent on the original image size.
          save_path {str} -- Save path for extracted face image. (default: {None})
  
      Returns:
          torch.tensor -- tensor representing the extracted face.
      """
      margin = [
          margin * (box[2] - box[0]) / (image_size - margin),
          margin * (box[3] - box[1]) / (image_size - margin),
      ]
      raw_image_size = get_size(img)
      box = [
          int(max(box[0] - margin[0] / 2, 0)),
          int(max(box[1] - margin[1] / 2, 0)),
          int(min(box[2] + margin[0] / 2, raw_image_size[0])),
          int(min(box[3] + margin[1] / 2, raw_image_size[1])),
      ]
  
      face = crop_resize(img, box, image_size)
  
      if save_path is not None:
          os.makedirs(os.path.dirname(save_path) + "/", exist_ok=True)
          save_img(face, save_path)
  
      face = F.to_tensor(np.float32(face))
  
      return face
  
imports: import torch
  from torch.nn.functional import interpolate
  from torchvision.transforms import functional
  from torchvision.ops.boxes import batched_nms
  from PIL import Image
  import numpy
  import os
  import math
label_desc: Use image node_id 13 for calling a global function with example usage: extract_face(img, box, image_size, margin, save_path) and returns: face