code: def train(sess, model, eval_model, train_set, valid_set, test_set):
      """Train a sketch-rnn model."""
      # Setup summary writer.
      summary_writer = tf.summary.FileWriter(FLAGS.log_root)
  
      # Calculate trainable params.
      t_vars = tf.trainable_variables()
      count_t_vars = 0
      for var in t_vars:
          num_param = np.prod(var.get_shape().as_list())
          count_t_vars += num_param
          tf.logging.info(
              "%s %s %i", var.name, str(var.get_shape()), num_param
          )
      tf.logging.info("Total trainable variables %i.", count_t_vars)
      model_summ = tf.summary.Summary()
      model_summ.value.add(
          tag="Num_Trainable_Params", simple_value=float(count_t_vars)
      )
      summary_writer.add_summary(model_summ, 0)
      summary_writer.flush()
  
      # setup eval stats
      best_valid_cost = 100000000.0  # set a large init value
      valid_cost = 0.0
  
      # main train loop
  
      hps = model.hps
      start = time.time()
  
      for _ in range(hps.num_steps):
          step = sess.run(model.global_step)
  
          curr_learning_rate = (
              hps.learning_rate - hps.min_learning_rate
          ) * (hps.decay_rate) ** step + hps.min_learning_rate
          curr_kl_weight = (
              hps.kl_weight
              - (hps.kl_weight - hps.kl_weight_start)
              * (hps.kl_decay_rate) ** step
          )
  
          _, x, s = train_set.random_batch()
          feed = {
              model.input_data: x,
              model.sequence_lengths: s,
              model.lr: curr_learning_rate,
              model.kl_weight: curr_kl_weight,
          }
  
          (train_cost, r_cost, kl_cost, _, train_step, _) = sess.run(
              [
                  model.cost,
                  model.r_cost,
                  model.kl_cost,
                  model.final_state,
                  model.global_step,
                  model.train_op,
              ],
              feed,
          )
  
          if step % 20 == 0 and step > 0:
              end = time.time()
              time_taken = end - start
  
              cost_summ = tf.summary.Summary()
              cost_summ.value.add(
                  tag="Train_Cost", simple_value=float(train_cost)
              )
              reconstr_summ = tf.summary.Summary()
              reconstr_summ.value.add(
                  tag="Train_Reconstr_Cost", simple_value=float(r_cost)
              )
              kl_summ = tf.summary.Summary()
              kl_summ.value.add(
                  tag="Train_KL_Cost", simple_value=float(kl_cost)
              )
              lr_summ = tf.summary.Summary()
              lr_summ.value.add(
                  tag="Learning_Rate",
                  simple_value=float(curr_learning_rate),
              )
              kl_weight_summ = tf.summary.Summary()
              kl_weight_summ.value.add(
                  tag="KL_Weight", simple_value=float(curr_kl_weight)
              )
              time_summ = tf.summary.Summary()
              time_summ.value.add(
                  tag="Time_Taken_Train", simple_value=float(time_taken)
              )
  
              output_format = (
                  "step: %d, lr: %.6f, klw: %0.4f, cost: %.4f, "
                  "recon: %.4f, kl: %.4f, train_time_taken: %.4f"
              )
              output_values = (
                  step,
                  curr_learning_rate,
                  curr_kl_weight,
                  train_cost,
                  r_cost,
                  kl_cost,
                  time_taken,
              )
              output_log = output_format % output_values
  
              tf.logging.info(output_log)
  
              summary_writer.add_summary(cost_summ, train_step)
              summary_writer.add_summary(reconstr_summ, train_step)
              summary_writer.add_summary(kl_summ, train_step)
              summary_writer.add_summary(lr_summ, train_step)
              summary_writer.add_summary(kl_weight_summ, train_step)
              summary_writer.add_summary(time_summ, train_step)
              summary_writer.flush()
              start = time.time()
  
          if step % hps.save_every == 0 and step > 0:
              (
                  valid_cost,
                  valid_r_cost,
                  valid_kl_cost,
              ) = evaluate_model(sess, eval_model, valid_set)
  
              end = time.time()
              time_taken_valid = end - start
              start = time.time()
  
              valid_cost_summ = tf.summary.Summary()
              valid_cost_summ.value.add(
                  tag="Valid_Cost", simple_value=float(valid_cost)
              )
              valid_reconstr_summ = tf.summary.Summary()
              valid_reconstr_summ.value.add(
                  tag="Valid_Reconstr_Cost",
                  simple_value=float(valid_r_cost),
              )
              valid_kl_summ = tf.summary.Summary()
              valid_kl_summ.value.add(
                  tag="Valid_KL_Cost", simple_value=float(valid_kl_cost)
              )
              valid_time_summ = tf.summary.Summary()
              valid_time_summ.value.add(
                  tag="Time_Taken_Valid",
                  simple_value=float(time_taken_valid),
              )
  
              output_format = (
                  "best_valid_cost: %0.4f, valid_cost: %.4f, valid_recon: "
                  "%.4f, valid_kl: %.4f, valid_time_taken: %.4f"
              )
              output_values = (
                  min(best_valid_cost, valid_cost),
                  valid_cost,
                  valid_r_cost,
                  valid_kl_cost,
                  time_taken_valid,
              )
              output_log = output_format % output_values
  
              tf.logging.info(output_log)
  
              summary_writer.add_summary(valid_cost_summ, train_step)
              summary_writer.add_summary(
                  valid_reconstr_summ, train_step
              )
              summary_writer.add_summary(valid_kl_summ, train_step)
              summary_writer.add_summary(valid_time_summ, train_step)
              summary_writer.flush()
  
              if valid_cost < best_valid_cost:
                  best_valid_cost = valid_cost
  
                  save_model(sess, FLAGS.log_root, step)
  
                  end = time.time()
                  time_taken_save = end - start
                  start = time.time()
  
                  tf.logging.info(
                      "time_taken_save %4.4f.", time_taken_save
                  )
  
                  best_valid_cost_summ = tf.summary.Summary()
                  best_valid_cost_summ.value.add(
                      tag="Best_Valid_Cost",
                      simple_value=float(best_valid_cost),
                  )
  
                  summary_writer.add_summary(
                      best_valid_cost_summ, train_step
                  )
                  summary_writer.flush()
  
                  (
                      eval_cost,
                      eval_r_cost,
                      eval_kl_cost,
                  ) = evaluate_model(sess, eval_model, test_set)
  
                  end = time.time()
                  time_taken_eval = end - start
                  start = time.time()
  
                  eval_cost_summ = tf.summary.Summary()
                  eval_cost_summ.value.add(
                      tag="Eval_Cost", simple_value=float(eval_cost)
                  )
                  eval_reconstr_summ = tf.summary.Summary()
                  eval_reconstr_summ.value.add(
                      tag="Eval_Reconstr_Cost",
                      simple_value=float(eval_r_cost),
                  )
                  eval_kl_summ = tf.summary.Summary()
                  eval_kl_summ.value.add(
                      tag="Eval_KL_Cost",
                      simple_value=float(eval_kl_cost),
                  )
                  eval_time_summ = tf.summary.Summary()
                  eval_time_summ.value.add(
                      tag="Time_Taken_Eval",
                      simple_value=float(time_taken_eval),
                  )
  
                  output_format = (
                      "eval_cost: %.4f, eval_recon: %.4f, "
                      "eval_kl: %.4f, eval_time_taken: %.4f"
                  )
                  output_values = (
                      eval_cost,
                      eval_r_cost,
                      eval_kl_cost,
                      time_taken_eval,
                  )
                  output_log = output_format % output_values
  
                  tf.logging.info(output_log)
  
                  summary_writer.add_summary(eval_cost_summ, train_step)
                  summary_writer.add_summary(
                      eval_reconstr_summ, train_step
                  )
                  summary_writer.add_summary(eval_kl_summ, train_step)
                  summary_writer.add_summary(eval_time_summ, train_step)
                  summary_writer.flush()
  
imports: import json
  import os
  import time
  import zipfile
  from magenta.models.sketch_rnn import model
  from magenta.models.sketch_rnn import utils
  import numpy
  import requests
  import six
  from six.moves.urllib.request import urlretrieve
  import tensorflow.compat.v1
label_desc: Use image node_id 9 for calling a global function with example usage: train(sess, model, eval_model, train_set, valid_set, test_set) without return types