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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 predictor(enc_flat, action, lstm_states, pred_depth, reuse=False, scope_prefix='', hparams=None):
 """LSTM predictor network."""

  with tf.variable_scope(scope_prefix + 'predict', reuse=reuse):

    enc_final_size = enc_flat.get_shape().as_list()[1]
    action_size = action.get_shape().as_list()[1]
    initial_size = (enc_final_size + action_size)

    batch_size = tf.shape(enc_flat)[0]

    init_stddev = 1e-2

    pre_pred = tf.concat([enc_flat, action], 1)
    pre_pred = tf.layers.dense(
        pre_pred,
        initial_size,
        kernel_initializer=tf.truncated_normal_initializer(stddev=init_stddev))

    # This is only needed or the GAN version.
    if hparams.pred_noise_std > 0:
      # Add the noise like this so a pretrained model can be used.
      pred_noise = tf.random_normal(
          shape=[batch_size, 100], stddev=hparams.pred_noise_std)
      pre_pred += tf.layers.dense(
          pred_noise,
          initial_size,
          kernel_initializer=tf.truncated_normal_initializer(
              stddev=init_stddev),
          name='noise_dense')

    pre_pred = tf.nn.relu(pre_pred)

    if lstm_states[pred_depth - 2] is None:
      back_connect = tf.tile(
          tf.get_variable(
              'back_connect_init',
              shape=[1, initial_size * 2],
              initializer=tf.truncated_normal_initializer(stddev=init_stddev))
          , (batch_size, 1))
    else:
      back_connect = lstm_states[pred_depth - 2]

    lstm_init_stddev = 1e-4

    part_pred, lstm_states[0] = common_video.lstm_cell(
        tf.concat([pre_pred, back_connect], 1),
        lstm_states[0],
        initial_size,
        use_peepholes=True,
        initializer=tf.truncated_normal_initializer(stddev=lstm_init_stddev),
        num_proj=initial_size)
    part_pred = tf.contrib.layers.layer_norm(part_pred)
    pred = part_pred

    for pred_layer_num in range(1, pred_depth, 2):
      part_pred, lstm_states[pred_layer_num] = common_video.lstm_cell(
          pred,
          lstm_states[pred_layer_num],
          initial_size,
          use_peepholes=True,
          initializer=tf.truncated_normal_initializer(stddev=lstm_init_stddev),
          num_proj=initial_size)
      pred += part_pred

      part_pred, lstm_states[pred_layer_num + 1] = common_video.lstm_cell(
          tf.concat([pred, pre_pred], 1),
          lstm_states[pred_layer_num + 1],
          initial_size,
          use_peepholes=True,
          initializer=tf.truncated_normal_initializer(stddev=lstm_init_stddev),
          num_proj=initial_size)
      part_pred = tf.contrib.layers.layer_norm(part_pred)
      pred += part_pred

    pred = tf.layers.dense(
        pred,
        enc_final_size,
        kernel_initializer=tf.truncated_normal_initializer(stddev=init_stddev))

    if hparams.enc_pred_use_l2norm:
      pred = tf.nn.l2_normalize(pred, 1)

    return pred