code: def train_teacher(dataset, nb_teachers, teacher_id):
      """
      This function trains a teacher (teacher id) among an ensemble of nb_teachers
      models for the dataset specified.
      :param dataset: string corresponding to dataset (svhn, cifar10)
      :param nb_teachers: total number of teachers in the ensemble
      :param teacher_id: id of the teacher being trained
      :return: True if everything went well
      """
      # If working directories do not exist, create them
      assert input.create_dir_if_needed(FLAGS.data_dir)
      assert input.create_dir_if_needed(FLAGS.train_dir)
  
      # Load the dataset
      if dataset == "svhn":
          (
              train_data,
              train_labels,
              test_data,
              test_labels,
          ) = input.ld_svhn(extended=True)
      elif dataset == "cifar10":
          (
              train_data,
              train_labels,
              test_data,
              test_labels,
          ) = input.ld_cifar10()
      elif dataset == "mnist":
          (
              train_data,
              train_labels,
              test_data,
              test_labels,
          ) = input.ld_mnist()
      else:
          print("Check value of dataset flag")
          return False
  
      # Retrieve subset of data for this teacher
      data, labels = input.partition_dataset(
          train_data, train_labels, nb_teachers, teacher_id
      )
  
      print("Length of training data: " + str(len(labels)))
  
      # Define teacher checkpoint filename and full path
      if FLAGS.deeper:
          filename = (
              str(nb_teachers)
              + "_teachers_"
              + str(teacher_id)
              + "_deep.ckpt"
          )
      else:
          filename = (
              str(nb_teachers)
              + "_teachers_"
              + str(teacher_id)
              + ".ckpt"
          )
      ckpt_path = FLAGS.train_dir + "/" + str(dataset) + "_" + filename
  
      # Perform teacher training
      assert deep_cnn.train(data, labels, ckpt_path)
  
      # Append final step value to checkpoint for evaluation
      ckpt_path_final = ckpt_path + "-" + str(FLAGS.max_steps - 1)
  
      # Retrieve teacher probability estimates on the test data
      teacher_preds = deep_cnn.softmax_preds(test_data, ckpt_path_final)
  
      # Compute teacher accuracy
      precision = metrics.accuracy(teacher_preds, test_labels)
      print("Precision of teacher after training: " + str(precision))
  
      return True
  
file_path: privacy/research/pate_2017/train_teachers.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import deep_cnn
  import input
  import metrics
  import tensorflow.compat.v1
label_desc: Use image node_id 1 for calling a global function with example usage: train_teacher(dataset, nb_teachers, teacher_id) and returns: True, False