code: def train(
      batch_size, learning_rate, num_training_iters, validation_steps
  ):
      """Runs the training."""
      print("Loading data")
      training_data, validation_data, test_data = dataset.load()
      print(
          "Loaded dataset with {} training, {} validation and {} test examples.".format(
              len(training_data[0]),
              len(validation_data[0]),
              len(test_data[0]),
          )
      )
  
      assert len(training_data[0]) % batch_size == 0
      assert len(validation_data[0]) % batch_size == 0
      assert len(test_data[0]) % batch_size == 0
  
      def build_iterator(data, infinite=True):
          """Build the iterator for inputs."""
          index = 0
          size = len(data[0])
          while True:
              if index + batch_size > size:
                  if infinite:
                      index = 0
                  else:
                      return
              yield data[0][index : index + batch_size], data[1][
                  index : index + batch_size
              ]
              index += batch_size
  
      train_iter = build_iterator(training_data)
      model = model_lib.Model([30])
  
      for i in range(num_training_iters):
          train_x, train_y = next(train_iter)
          model.train(train_x, train_y, learning_rate)
          if (i + 1) % validation_steps == 0:
              validation_iter = build_iterator(
                  validation_data, infinite=False
              )
              correct_predictions = 0
              for valid_x, valid_y in validation_iter:
                  correct_predictions += model.evaluate(
                      valid_x, valid_y
                  )
              print(
                  "{}/{} correct validation predictions.".format(
                      correct_predictions, len(validation_data[0])
                  )
              )
  
imports: from absl import app
  from absl import flags
  from six.moves import range
  import tensorflow.compat.v2
  from trax.tf_numpy.examples.mnist import dataset
  from trax.tf_numpy.examples.mnist import model
label_desc: Use image node_id 1 for calling a global function with example usage: train(batch_size, learning_rate, num_training_iters, validation_steps) without return types