class_docstr: Stochastic Variational Video Prediction From Basic Model!
class_docstr_tok: ["Stochastic","Variational","Video","Prediction","From","Basic","Model","!"]
docstr: Video wide latent.
docstr_tok: ["Video","wide","latent","."]
code: def video_features(
      self, all_frames, all_actions, all_rewards, all_raw_frames
  ):
      """Video wide latent."""
      del all_actions, all_rewards, all_raw_frames
      if not self.hparams.stochastic_model:
          return None, None, None
      frames = tf.stack(all_frames, axis=1)
      mean, std = self.construct_latent_tower(frames, time_axis=1)
      latent = common_video.get_gaussian_tensor(mean, std)
      return [latent, mean, std]
  
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 10 for calling the NextFrameSv2p obj's underlying member method code with example usage: obj.video_features(all_frames, all_actions, all_rewards, all_raw_frames) and returns: list, None,  None,  None