code: def main(unused_argv):
      del unused_argv
  
      # Load Config
      config_name = FLAGS.config
      config_module = importlib.import_module(
          configs_module_prefix + ".%s" % config_name
      )
      config = config_module.config
      model_uid = common.get_model_uid(config_name, FLAGS.exp_uid)
      batch_size = config["batch_size"]
  
      # Load dataset
      dataset = common.load_dataset(config)
      save_path = dataset.save_path
      train_data = dataset.train_data
      attr_train = dataset.attr_train
      eval_data = dataset.eval_data
      attr_eval = dataset.attr_eval
  
      # Make the directory
      save_dir = os.path.join(save_path, model_uid)
      best_dir = os.path.join(save_dir, "best")
      tf.gfile.MakeDirs(save_dir)
      tf.gfile.MakeDirs(best_dir)
      tf.logging.info("Save Dir: %s", save_dir)
  
      np.random.seed(10003)
      N_train = train_data.shape[0]
      N_eval = eval_data.shape[0]
  
      # Load Model
      tf.reset_default_graph()
      sess = tf.Session()
      m = model_dataspace.Model(config, name=model_uid)
      _ = m()  # noqa
  
      # Create summaries
      y_true = m.labels
      y_pred = tf.cast(tf.greater(m.pred_classifier, 0.5), tf.int32)
      accuracy = tf.reduce_mean(
          tf.cast(tf.equal(y_true, y_pred), tf.float32)
      )
  
      tf.summary.scalar("Loss", m.classifier_loss)
      tf.summary.scalar("Accuracy", accuracy)
      scalar_summaries = tf.summary.merge_all()
  
      # Summary writers
      train_writer = tf.summary.FileWriter(
          save_dir + "/train", sess.graph
      )
      eval_writer = tf.summary.FileWriter(
          save_dir + "/eval", sess.graph
      )
  
      # Initialize
      sess.run(tf.global_variables_initializer())
  
      i_start = 0
      running_N_eval = 30
      traces = {
          "i": [],
          "i_pred": [],
          "loss": [],
          "loss_eval": [],
      }
  
      best_eval_loss = np.inf
      classifier_lr_ = np.logspace(
          np.log10(FLAGS.lr), np.log10(1e-6), FLAGS.n_iters
      )
  
      # Train the Classifier
      for i in range(i_start, FLAGS.n_iters):
          start = (i * batch_size) % N_train
          end = start + batch_size
          batch = train_data[start:end]
          labels = attr_train[start:end]
  
          # train op
          res = sess.run(
              [m.train_classifier, m.classifier_loss, scalar_summaries],
              {
                  m.x: batch,
                  m.labels: labels,
                  m.classifier_lr: classifier_lr_[i],
              },
          )
          tf.logging.info("Iter: %d, Loss: %.2e", i, res[1])
          train_writer.add_summary(res[-1], i)
  
          if i % 10 == 0:
              # write training reconstructions
              if batch.shape[0] == batch_size:
                  # write eval summaries
                  start = (i * batch_size) % N_eval
                  end = start + batch_size
                  batch = eval_data[start:end]
                  labels = attr_eval[start:end]
  
                  if batch.shape[0] == batch_size:
                      res_eval = sess.run(
                          [m.classifier_loss, scalar_summaries],
                          {
                              m.x: batch,
                              m.labels: labels,
                          },
                      )
                      traces["loss_eval"].append(res_eval[0])
                      eval_writer.add_summary(res_eval[-1], i)
  
          if i % FLAGS.n_iters_per_save == 0:
              smoothed_eval_loss = np.mean(
                  traces["loss_eval"][-running_N_eval:]
              )
              if smoothed_eval_loss < best_eval_loss:
                  # Save the best model
                  best_eval_loss = smoothed_eval_loss
                  save_name = os.path.join(
                      best_dir, "classifier_best_%s.ckpt" % model_uid
                  )
                  tf.logging.info(
                      "SAVING BEST! %s Iter: %d", save_name, i
                  )
                  m.classifier_saver.save(sess, save_name)
                  with tf.gfile.Open(
                      os.path.join(best_dir, "best_ckpt_iters.txt"), "w"
                  ) as f:
                      f.write("%d" % i)
  
imports: import importlib
  import os
  from magenta.models.latent_transfer import common
  from magenta.models.latent_transfer import model_dataspace
  import numpy
  import tensorflow.compat.v1
label_desc: Use image node_id 1 for calling a global function with example usage: main(unused_argv) without return types