code: def load_jpeg_from_file(path, cuda=True):
      img_transforms = transforms.Compose(
          [
              transforms.Resize(256),
              transforms.CenterCrop(224),
              transforms.ToTensor(),
          ]
      )
  
      img = img_transforms(Image.open(path))
      with torch.no_grad():
          # mean and std are not multiplied by 255 as they are in training script
          # torch dataloader reads data into bytes whereas loading directly
          # through PIL creates a tensor with floats in [0,1] range
          mean = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)
          std = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)
  
          if cuda:
              mean = mean.cuda()
              std = std.cuda()
              img = img.cuda()
          img = img.float()
  
          input = img.unsqueeze(0).sub_(mean).div_(std)
  
      return input
  
imports: import os
  import torch
  import numpy
  from PIL import Image
  from functools import partial
  from image_classification.autoaugment import AutoaugmentImageNetPolicy
  import torchvision.datasets
  import torchvision.transforms
label_desc: Use image node_id 1 for calling a global function with example usage: load_jpeg_from_file(path, cuda) and returns: input