class_docstr: A simple environment model.
class_docstr_tok: ["A","simple","environment","model","."]
code: def step(self, action: types.Action):
      # Reset if required.
      if self._needs_reset:
          raise ValueError(
              "Model must be reset with an initial timestep."
          )
  
      # Step the model.
      state, action = tf2_utils.add_batch_dim([self._state, action])
      new_state, reward, discount_logits = [
          x.numpy().squeeze(axis=0)
          for x in self._forward(state, action)
      ]
      discount = special.softmax(discount_logits)
  
      # Save the resulting state for the next step.
      self._state = new_state
  
      # We threshold discount on a given tolerance.
      if discount < self._terminal_tol:
          self._needs_reset = True
          return dm_env.termination(
              reward=reward, observation=self._state.copy()
          )
      return dm_env.transition(
          reward=reward, observation=self._state.copy()
      )
  
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 3 for calling the MLPModel obj's underlying member method code with example usage: obj.step(action) and returns: dm_env, dm_env