code: def extract_svhn(local_url):
      """Extract a MATLAB matrix into two numpy arrays with data and labels."""
  
      with tf.gfile.Open(local_url, mode="r") as file_obj:
          # Load MATLAB matrix using scipy IO
          data_dict = loadmat(file_obj)
  
          # Extract each dictionary (one for data, one for labels)
          data, labels = data_dict["X"], data_dict["y"]
  
          # Set np type
          data = np.asarray(data, dtype=np.float32)
          labels = np.asarray(labels, dtype=np.int32)
  
          # Transpose data to match TF model input format
          data = data.transpose(3, 0, 1, 2)
  
          # Fix the SVHN labels which label 0s as 10s
          labels[labels == 10] = 0
  
          # Fix label dimensions
          labels = labels.reshape(len(labels))
  
          return data, labels
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import gzip
  import math
  import os
  import sys
  import tarfile
  import numpy
  from scipy.io import loadmat
  from six.moves import cPickle
  from six.moves import urllib
  from six.moves import xrange
  import tensorflow.compat.v1
label_desc: Use image node_id 4 for calling a global function with example usage: extract_svhn(local_url) and returns: data,  labels