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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code: def __init__(self, *args, **kwargs):
      super(NextFrameBase, self).__init__(*args, **kwargs)
      self.internal_states = self.init_internal_states()
  
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 9 to create a new NextFrameBase object from inherited base classes: t2t_model with example: obj = NextFrameBase()