class_docstr: Base class for next_frame models.
  
  This is the base class for the models that given the previous frames
  can predict the next frame. They may also support reward prediction
  and action condition prediction which enables them to be run as
  a world model in model-based RL pipeline.
  
  The API supports both recurrent and stacked frames models. Please look
  at the documents for next_frame function for the API.
  
  If you are implementing a next frame prediction model consider
  following the API presented in this class. But if the API
  is too limiting for your models, feel free to override lower level
  functions and/or inheret from T2TModel directly.
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docstr: Optional video wide features.
  
    If the model requires access to all of the video frames
    (e.g. in case of approximating one latent for the whole video)
    override this function to add them. They will be accessible
    as video_features in next_frame function.
  
  Args:
    all_frames: list of all frames including input and target frames.
    all_actions: list of all actions including input and target actions.
    all_rewards: list of all rewards including input and target rewards.
    all_raw_frames: list of all raw frames (before modalities).
  
  Returns:
    video_features: a dictionary containing video-wide features.
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code: def video_features(
      self, all_frames, all_actions, all_rewards, all_raw_frames
  ):
      """Optional video wide features.
  
        If the model requires access to all of the video frames
        (e.g. in case of approximating one latent for the whole video)
        override this function to add them. They will be accessible
        as video_features in next_frame function.
  
      Args:
        all_frames: list of all frames including input and target frames.
        all_actions: list of all actions including input and target actions.
        all_rewards: list of all rewards including input and target rewards.
        all_raw_frames: list of all raw frames (before modalities).
  
      Returns:
        video_features: a dictionary containing video-wide features.
      """
      del all_frames, all_actions, all_rewards, all_raw_frames
      return None
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import six
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import common_video
  from tensor2tensor.layers import discretization
  from tensor2tensor.layers import modalities
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
label_desc: Use image node_id 2 for calling the NextFrameBase obj's underlying member method code with example usage: obj.video_features(all_frames, all_actions, all_rewards, all_raw_frames) and returns: None