class_docstr: Stochastic Variational Video Prediction From Basic Model!
class_docstr_tok: ["Stochastic","Variational","Video","Prediction","From","Basic","Model","!"]
docstr: Builds a reward prediction network.
docstr_tok: ["Builds","a","reward","prediction","network","."]
code: def reward_prediction_big(
      self, input_images, input_reward, action, latent, mid_outputs
  ):
      """Builds a reward prediction network."""
      del mid_outputs
      conv_size = self.tinyify([32, 32, 16, 8])
  
      with tf.variable_scope("reward_pred", reuse=tf.AUTO_REUSE):
          x = tf.concat(input_images, axis=3)
          x = tfcl.layer_norm(x)
  
          if not self.hparams.small_mode:
              x = tfl.conv2d(
                  x,
                  conv_size[1],
                  [3, 3],
                  strides=(2, 2),
                  activation=tf.nn.relu,
                  name="reward_conv1",
              )
              x = tfcl.layer_norm(x)
  
          # Inject additional inputs
          if action is not None:
              x = common_video.inject_additional_input(
                  x, action, "action_enc", self.hparams.action_injection
              )
          if input_reward is not None:
              x = common_video.inject_additional_input(
                  x, input_reward, "reward_enc"
              )
          if latent is not None:
              latent = tfl.flatten(latent)
              latent = tf.expand_dims(latent, axis=1)
              latent = tf.expand_dims(latent, axis=1)
              x = common_video.inject_additional_input(
                  x, latent, "latent_enc"
              )
  
          x = tfl.conv2d(
              x,
              conv_size[2],
              [3, 3],
              strides=(2, 2),
              activation=tf.nn.relu,
              name="reward_conv2",
          )
          x = tfcl.layer_norm(x)
          x = tfl.conv2d(
              x,
              conv_size[3],
              [3, 3],
              strides=(2, 2),
              activation=tf.nn.relu,
              name="reward_conv3",
          )
      return x
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import common_video
  from tensor2tensor.layers import discretization
  from tensor2tensor.models.video import base
  from tensor2tensor.models.video import base_vae
  from tensor2tensor.utils import contrib
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
label_desc: Use image node_id 7 for calling the NextFrameSv2p obj's underlying member method code with example usage: obj.reward_prediction_big(input_images, input_reward, action, latent, mid_outputs) and returns: x