//  [python]
def fit(self,
          dataset,
          nb_epoch=10,
          max_checkpoints_to_keep=5,
          log_every_N_batches=50,
          checkpoint_interval=10,
          **kwargs):
    """Fit the model.

    Parameters
    ----------
    dataset: dc.data.Dataset
      Dataset object holding training data
    nb_epoch: 10
      Number of training epochs.
    max_checkpoints_to_keep: int
      Maximum number of checkpoints to keep; older checkpoints will be deleted.
    log_every_N_batches: int
      Report every N batches. Useful for training on very large datasets,
      where epochs can take long time to finish.
    checkpoint_interval: int
      Frequency at which to write checkpoints, measured in epochs

    Raises
    ------
    AssertionError
      If model is not in training mode.
    """
    ############################################################## TIMING
    time1 = time.time()
    ############################################################## TIMING
    log("Training for %d epochs" % nb_epoch, self.verbose)
    with self.train_graph.graph.as_default():
      train_op = self.get_training_op(self.train_graph.graph,
                                      self.train_graph.loss)
      with self._get_shared_session(train=True) as sess:
        sess.run(tf.global_variables_initializer())
        saver = tf.train.Saver(max_to_keep=max_checkpoints_to_keep)
        # Save an initial checkpoint.
        saver.save(sess, self._save_path, global_step=0)

        # Define the code that runs on a separate thread to feed data into the queue.
        def enqueue(sess, dataset, nb_epoch, epoch_end_indices):
          index = 0
          for epoch in range(nb_epoch):
            for X_b, y_b, w_b, ids_b in dataset.iterbatches(
                self.batch_size, pad_batches=self.pad_batches):
              feed_dict = self.construct_feed_dict(X_b, y_b, w_b, ids_b)
              sess.run(self.train_graph.graph.enqueue, feed_dict=feed_dict)
              index += 1
            epoch_end_indices.append(index)
          sess.run(self.train_graph.graph.queue.close())

        epoch_end_indices = []
        enqueue_thread = threading.Thread(
            target=enqueue, args=[sess, dataset, nb_epoch, epoch_end_indices])
        enqueue_thread.daemon = True
        enqueue_thread.start()

        # Main training loop.
        try:
          epoch = 0
          index = 0
          index_in_epoch = 0
          avg_loss = 0.0
          while True:
            if index_in_epoch % log_every_N_batches == 0:
              log("On batch %d" % index_in_epoch, self.verbose)
            # Run training op.
            fetches = self.train_graph.output + [
                train_op, self.train_graph.loss
            ]
            fetched_values = sess.run(fetches)
            loss = fetched_values[-1]
            avg_loss += loss
            index += 1
            index_in_epoch += 1
            if len(epoch_end_indices) > 0 and index >= epoch_end_indices[0]:
              # We have reached the end of an epoch.
              if epoch % checkpoint_interval == checkpoint_interval - 1:
                saver.save(sess, self._save_path, global_step=epoch)
              avg_loss = float(avg_loss) / index_in_epoch
              log('Ending epoch %d: Average loss %g' % (epoch, avg_loss),
                  self.verbose)
              epoch += 1
              index_in_epoch = 0
              avg_loss = 0.0
              del epoch_end_indices[0]
        except tf.errors.OutOfRangeError:
          # We have reached the end of the data.
          pass
        # Always save a final checkpoint when complete.
        saver.save(sess, self._save_path, global_step=epoch + 1)
    ############################################################## TIMING
    time2 = time.time()
    print("TIMING: model fitting took %0.3f s" % (time2 - time1), self.verbose)