class_docstr: Stochastic Variational Video Prediction From Basic Model!
class_docstr_tok: ["Stochastic","Variational","Video","Prediction","From","Basic","Model","!"]
code: def reward_prediction(self, *args, **kwargs):
      model = self.hparams.reward_model
      if model == "basic":
          return self.reward_prediction_basic(*args, **kwargs)
      elif model == "big":
          return self.reward_prediction_big(*args, **kwargs)
      elif model == "mid":
          return self.reward_prediction_mid(*args, **kwargs)
      else:
          raise ValueError("Unknown reward model %s" % model)
  
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 4 for calling the NextFrameSv2p obj's underlying member method code with example usage: obj.reward_prediction() and returns: self, self, self