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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def construct_model(images, actions=None, context_frames=2, hparams=None, is_training=True):
 """Constructs the tensorflow graph of the hierarchical model."""


  pred_depth = 20

  enc_out_all, pred_out_all, van_out_all, van_on_enc_all = [], [], [], []

  lstm_states = [None] * (pred_depth + 2)

  enc_out = encoder_vgg(
      images[0], hparams.enc_size, False, scope_prefix='timestep/',
      hparams=hparams, is_training=is_training)
  enc_out = tf.identity(enc_out, 'enc_out')
  enc_out_all.append(enc_out)

  num_timesteps = len(actions) - 1
  sum_freq = int(num_timesteps / 4 + 1)

  reuse = False
  for timestep, action in zip(range(len(actions) - 1), actions[:-1]):
    done_warm_start = timestep > context_frames - 1

    with tf.variable_scope('timestep', reuse=reuse):
      if done_warm_start:
        pred_input = pred_out_all[-1]
      else:
        pred_input = enc_out_all[-1]
      pred_out = predictor(
          pred_input, action, lstm_states, pred_depth, False, hparams=hparams)
      pred_out = tf.identity(pred_out, 'pred_out')
      if timestep % sum_freq == 0:  # and not hparams.use_tpu:
        tf.summary.histogram('pred_out', pred_out)
      pred_out_all.append(pred_out)

      if timestep % sum_freq == 0:  # and not hparams.use_tpu:
        tf.summary.histogram('lstm_state', lstm_states[0])
      van_out, _, _ = van(
          enc_out_all[0],
          images[0],
          pred_out,
          images[timestep + 1],
          tf.AUTO_REUSE,
          hparams=hparams)
      van_out = tf.identity(van_out, 'van_out')
      van_out_all.append(van_out)

      enc_out = encoder_vgg(
          images[timestep + 1], hparams.enc_size, True, hparams=hparams,
          is_training=is_training)
      enc_out = tf.identity(enc_out, 'enc_out')
      if timestep % sum_freq == 0:  # and not hparams.use_tpu:
        tf.summary.histogram('enc_out', enc_out)
      enc_out_all.append(enc_out)

      van_input = images[0]
      enc_noise = tf.zeros_like(enc_out)
      if timestep % sum_freq == 0:  # and not hparams.use_tpu:
        tf.summary.histogram('enc_noise', enc_noise)
      van_on_enc, _, _ = van(
          enc_out_all[0],
          van_input,
          enc_out + enc_noise,
          images[timestep + 1],
          tf.AUTO_REUSE,
          hparams=hparams)
      van_on_enc = tf.identity(van_on_enc, 'van_on_enc')
      van_on_enc_all.append(van_on_enc)

      reuse = True

  return enc_out_all, pred_out_all, van_out_all, van_on_enc_all