code: def __init__(self, prefix, data_shape, batch_size=1):
      image_size = (112, 112)
      self.image_size = image_size
      weight = torch.load(prefix)
      resnet = eval("backbones.{}".format(args.network))(False).cuda()
      resnet.load_state_dict(weight)
      model = torch.nn.DataParallel(resnet)
      self.model = model
      self.model.eval()
      src = np.array(
          [
              [30.2946, 51.6963],
              [65.5318, 51.5014],
              [48.0252, 71.7366],
              [33.5493, 92.3655],
              [62.7299, 92.2041],
          ],
          dtype=np.float32,
      )
      src[:, 0] += 8.0
      self.src = src
      self.batch_size = batch_size
      self.data_shape = data_shape
  
imports: import os
  import pickle
  import matplotlib
  import pandas
  import matplotlib.pyplot
  import timeit
  import sklearn
  import argparse
  from sklearn.metrics import roc_curve, auc
  from menpo.visualize.viewmatplotlib import sample_colours_from_colourmap
  from prettytable import PrettyTable
  from pathlib import Path
  import sys
  import warnings
  import cv2
  import numpy
  import torch
  from skimage import transform
  import backbones
label_desc: Use image node_id 1 to create a new Embedding object from inherited base classes: object with example: obj = Embedding(prefix, data_shape, batch_size)