code: 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 = 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 = 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
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from six.moves import reduce
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import common_video
  from tensor2tensor.models.video import epva_params
  from tensor2tensor.models.video import sv2p
  from tensor2tensor.utils import contrib
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
  from tensorflow.contrib.framework.python.ops import arg_scope
  from tensorflow.contrib.slim.python.slim.nets import vgg
label_desc: Use image node_id 7 for calling a global function with example usage: predictor(enc_flat, action, lstm_states, pred_depth, reuse, scope_prefix, hparams) and returns: pred