code: def normalize_transform():
      """Returns a torchvision `transforms.Normalize
      <https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.Normalize>`_ object
      with default mean and standard deviation values as required by PyTorch's
      pre-trained models.
  
      :return: A transforms.Normalize object with pre-computed values.
      :rtype: torchvision.transforms.Normalize
  
      **Example**::
  
          >>> from detecto.core import Dataset
          >>> from detecto.utils import normalize_transform
          >>> from torchvision import transforms
  
          >>> # Note: if transform=None, the Dataset will automatically
          >>> # apply these default transforms to images
          >>> defaults = transforms.Compose([
          >>>     transforms.ToTensor(),
          >>>     normalize_transform(),
          >>> ])
          >>> dataset = Dataset('labels.csv', 'images/', transform=defaults)
      """
  
      # Default for PyTorch's pre-trained models
      return transforms.Normalize(
          mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
      )
  
file_path: detecto/detecto/utils.py
imports: import cv2
  import os
  import pandas
  import torch
  import xml.etree.ElementTree
  from glob import glob
  from torchvision import transforms
label_desc: Use image node_id 3 for calling a global function with example usage: normalize_transform() and returns: transforms