class_docstr: Stochastic Variational Video Prediction From Basic Model!
class_docstr_tok: ["Stochastic","Variational","Video","Prediction","From","Basic","Model","!"]
code: def reward_prediction_basic(
      self, input_images, input_reward, action, latent, mid_outputs
  ):
      del input_reward, action, latent, mid_outputs
      x = input_images
      x = tf.reduce_mean(x, axis=[1, 2], keepdims=True)
      x = tfl.dense(x, 128, activation=tf.nn.relu, name="reward_pred")
      x = tf.expand_dims(x, axis=3)
      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 5 for calling the NextFrameSv2p obj's underlying member method code with example usage: obj.reward_prediction_basic(input_images, input_reward, action, latent, mid_outputs) and returns: x