class_docstr: A simple environment model.
class_docstr_tok: ["A","simple","environment","model","."]
code: def update(
      self,
      timestep: dm_env.TimeStep,
      action: types.Action,
      next_timestep: dm_env.TimeStep,
  ) -> dm_env.TimeStep:
      # Add the true transition to replay.
      transition = [
          timestep.observation,
          action,
          next_timestep.reward,
          next_timestep.discount,
          next_timestep.observation,
      ]
      self._replay.add(transition)
  
      # Step the model to generate a synthetic transition.
      ts = self.step(action)
  
      # Copy the *true* state on update.
      self._state = next_timestep.observation.copy()
  
      if ts.last() or next_timestep.last():
          # Model believes that a termination has happened.
          # This will result in a crash during planning if the true environment
          # didn't terminate here as well. So, we indicate that we need a reset.
          self._needs_reset = True
  
      # Sample from replay and do SGD.
      if self._replay.size >= self._batch_size:
          batch = self._replay.sample(self._batch_size)
          self._step(*batch)
  
      return ts
  
imports: from typing import Optional, Tuple
  from acme import specs
  from acme.agents.tf.mcts import types
  from acme.agents.tf.mcts.models import base
  from acme.tf import utils
  from bsuite.baselines.utils import replay
  import dm_env
  import numpy
  from scipy import special
  import sonnet
  import tensorflow
label_desc: Use image node_id 5 for calling the MLPModel obj's underlying member method code with example usage: obj.update(timestep, action, next_timestep) and returns: ts