code: def _n_step_transition_from_episode(
      observations: types.NestedTensor,
      actions: tf.Tensor,
      rewards: tf.Tensor,
      discounts: tf.Tensor,
      n_step: int,
      additional_discount: float,
  ) -> types.Transition:
      """Produce Reverb-like N-step transition from a full episode.
  
      Observations, actions, rewards and discounts have the same length. This
      function will ignore the first reward and discount and the last action.
  
      Args:
        observations: [episode_length, ...] Tensor.
        actions: [episode_length, ...] Tensor.
        rewards: [episode_length] Tensor.
        discounts: [episode_length] Tensor.
        n_step: number of steps to squash into a single transition.
        additional_discount: discount to use for TD updates.
  
      Returns:
        A types.Transition.
      """
  
      max_index = tf.shape(rewards)[0] - 1
      first = tf.random.uniform(
          shape=(), minval=0, maxval=max_index - 1, dtype=tf.int32
      )
      last = tf.minimum(first + n_step, max_index)
  
      o_t = tree.map_structure(operator.itemgetter(first), observations)
      a_t = tree.map_structure(operator.itemgetter(first), actions)
      o_tp1 = tree.map_structure(
          operator.itemgetter(last), observations
      )
  
      # 0, 1, ..., n-1.
      discount_range = tf.cast(tf.range(last - first), tf.float32)
      # 1, g, ..., g^{n-1}.
      additional_discounts = tf.pow(additional_discount, discount_range)
      # 1, d_t, d_t * d_{t+1}, ..., d_t * ... * d_{t+n-2}.
      discounts = tf.concat(
          [[1.0], tf.math.cumprod(discounts[first : last - 1])], 0
      )
      # 1, g * d_t, ..., g^{n-1} * d_t * ... * d_{t+n-2}.
      discounts *= additional_discounts
      # r_t + g * d_t * r_{t+1} + ... + g^{n-1} * d_t * ... * d_{t+n-2} * r_{t+n-1}
      # We have to shift rewards by one so last=max_index corresponds to transitions
      # that include the last reward.
      r_t = tf.reduce_sum(rewards[first + 1 : last + 1] * discounts)
  
      # g^{n-1} * d_{t} * ... * d_{t+n-1}.
      d_t = discounts[-1]
  
      return types.Transition(o_t, a_t, r_t, d_t, o_tp1)
  
imports: import functools
  import operator
  from typing import Callable
  from acme import core
  from acme import environment_loop
  from acme import specs
  from acme import types
  from acme.agents.jax import actor_core
  from acme.agents.jax import actors
  from acme.agents.jax import bc
  from acme.agents.tf.dqfd import bsuite_demonstrations
  from acme.jax import networks
  from acme.jax import types
  from acme.jax import utils
  from acme.jax import variable_utils
  from acme.jax.deprecated import offline_distributed_layout
  from acme.utils import counting
  from acme.utils import loggers
  from acme.wrappers import single_precision
  import bsuite
  import dm_env
  import haiku
  import jax
  import jax.numpy
  from jax.scipy import special
  import rlax
  import tensorflow
  import tree
label_desc: Use image node_id 2 for calling a global function with example usage: _n_step_transition_from_episode(observations, actions, rewards, discounts, n_step, additional_discount) and returns: types