class_docstr: Basic next-frame model with a tiny discrete latent.
code: def init_internal_states(self):
      if not self.hparams.concat_internal_states:
          return None
      # Hardcoded frame shapes.
      max_batch_size = max(_MAX_BATCH, self.hparams.batch_size)
      shape = (
          [max_batch_size]
          + self.hparams.problem.frame_shape[:-1]
          + [self.hparams.recurrent_state_size]
      )
      with tf.variable_scope("clean_scope_for_internal_state"):
          v = tf.get_variable(
              "state",
              shape,
              trainable=False,
              initializer=tf.zeros_initializer(),
          )
      return [[v]]
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import common_video
  from tensor2tensor.layers import discretization
  from tensor2tensor.models.video import base_vae
  from tensor2tensor.models.video import basic_deterministic
  from tensor2tensor.models.video import basic_deterministic_params
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
label_desc: Use image node_id 2 for calling the NextFrameBasicStochasticDiscrete obj's underlying member method code with example usage: obj.init_internal_states() and returns: list, None