class_docstr: A simple environment model.
class_docstr_tok: ["A","simple","environment","model","."]
code: def _step(
      self,
      o_t: tf.Tensor,
      a_t: tf.Tensor,
      r_t: tf.Tensor,
      d_t: tf.Tensor,
      o_tp1: tf.Tensor,
  ) -> tf.Tensor:
      with tf.GradientTape() as tape:
          next_state, reward, discount = self._transition_model(
              o_t, a_t
          )
  
          state_loss = tf.square(next_state - o_tp1)
          reward_loss = tf.square(reward - r_t)
          discount_loss = tf.nn.sigmoid_cross_entropy_with_logits(
              d_t, discount
          )
  
          loss = sum(
              [
                  tf.reduce_mean(state_loss),
                  tf.reduce_mean(reward_loss),
                  tf.reduce_mean(discount_loss),
              ]
          )
  
      gradients = tape.gradient(loss, self._variables)
      self._optimizer.apply(gradients, self._variables)
  
      return loss
  
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 2 for calling the MLPModel obj's underlying member method code with example usage: obj._step(o_t, a_t, r_t, d_t, o_tp1) and returns: loss