code: def r2d2_loss(
      policy: Policy, model, _, train_batch: SampleBatch
  ) -> TensorType:
      """Constructs the loss for R2D2TFPolicy.
  
      Args:
          policy: The Policy to calculate the loss for.
          model (ModelV2): The Model to calculate the loss for.
          train_batch: The training data.
  
      Returns:
          TensorType: A single loss tensor.
      """
      config = policy.config
  
      # Construct internal state inputs.
      i = 0
      state_batches = []
      while "state_in_{}".format(i) in train_batch:
          state_batches.append(train_batch["state_in_{}".format(i)])
          i += 1
      assert state_batches
  
      # Q-network evaluation (at t).
      q, _, _, _ = compute_q_values(
          policy,
          model,
          train_batch,
          state_batches=state_batches,
          seq_lens=train_batch.get(SampleBatch.SEQ_LENS),
          explore=False,
          is_training=True,
      )
  
      # Target Q-network evaluation (at t+1).
      q_target, _, _, _ = compute_q_values(
          policy,
          policy.target_model,
          train_batch,
          state_batches=state_batches,
          seq_lens=train_batch.get(SampleBatch.SEQ_LENS),
          explore=False,
          is_training=True,
      )
  
      if not hasattr(policy, "target_q_func_vars"):
          policy.target_q_func_vars = policy.target_model.variables()
  
      actions = tf.cast(train_batch[SampleBatch.ACTIONS], tf.int64)
      dones = tf.cast(train_batch[SampleBatch.TERMINATEDS], tf.float32)
      rewards = train_batch[SampleBatch.REWARDS]
      weights = tf.cast(train_batch[PRIO_WEIGHTS], tf.float32)
  
      B = tf.shape(state_batches[0])[0]
      T = tf.shape(q)[0] // B
  
      # Q scores for actions which we know were selected in the given state.
      one_hot_selection = tf.one_hot(actions, policy.action_space.n)
      q_selected = tf.reduce_sum(
          tf.where(q > tf.float32.min, q, tf.zeros_like(q))
          * one_hot_selection,
          axis=1,
      )
  
      if config["double_q"]:
          best_actions = tf.argmax(q, axis=1)
      else:
          best_actions = tf.argmax(q_target, axis=1)
  
      best_actions_one_hot = tf.one_hot(
          best_actions, policy.action_space.n
      )
      q_target_best = tf.reduce_sum(
          tf.where(
              q_target > tf.float32.min,
              q_target,
              tf.zeros_like(q_target),
          )
          * best_actions_one_hot,
          axis=1,
      )
  
      if config["num_atoms"] > 1:
          raise ValueError("Distributional R2D2 not supported yet!")
      else:
          q_target_best_masked_tp1 = (1.0 - dones) * tf.concat(
              [q_target_best[1:], tf.constant([0.0])], axis=0
          )
  
          if config["use_h_function"]:
              h_inv = h_inverse(
                  q_target_best_masked_tp1, config["h_function_epsilon"]
              )
              target = h_function(
                  rewards + config["gamma"] ** config["n_step"] * h_inv,
                  config["h_function_epsilon"],
              )
          else:
              target = (
                  rewards
                  + config["gamma"] ** config["n_step"]
                  * q_target_best_masked_tp1
              )
  
          # Seq-mask all loss-related terms.
          seq_mask = tf.sequence_mask(
              train_batch[SampleBatch.SEQ_LENS], T
          )[:, :-1]
          # Mask away also the burn-in sequence at the beginning.
          burn_in = policy.config["replay_buffer_config"][
              "replay_burn_in"
          ]
          # Making sure, this works for both static graph and eager.
          if burn_in > 0:
              seq_mask = tf.cond(
                  pred=tf.convert_to_tensor(burn_in, tf.int32) < T,
                  true_fn=lambda: tf.concat(
                      [
                          tf.fill([B, burn_in], False),
                          seq_mask[:, burn_in:],
                      ],
                      1,
                  ),
                  false_fn=lambda: seq_mask,
              )
  
          def reduce_mean_valid(t):
              return tf.reduce_mean(tf.boolean_mask(t, seq_mask))
  
          # Make sure to use the correct time indices:
          # Q(t) - [gamma * r + Q^(t+1)]
          q_selected = tf.reshape(q_selected, [B, T])[:, :-1]
          td_error = q_selected - tf.stop_gradient(
              tf.reshape(target, [B, T])[:, :-1]
          )
          td_error = td_error * tf.cast(seq_mask, tf.float32)
          weights = tf.reshape(weights, [B, T])[:, :-1]
          policy._total_loss = reduce_mean_valid(
              weights * huber_loss(td_error)
          )
          # Store the TD-error per time chunk (b/c we need only one mean
          # prioritized replay weight per stored sequence).
          policy._td_error = tf.reduce_mean(td_error, axis=-1)
          policy._loss_stats = {
              "mean_q": reduce_mean_valid(q_selected),
              "min_q": tf.reduce_min(q_selected),
              "max_q": tf.reduce_max(q_selected),
              "mean_td_error": reduce_mean_valid(td_error),
          }
  
      return policy._total_loss
  
imports: from typing import Dict, List, Optional, Tuple
  import gymnasium
  import ray
  from ray.rllib.algorithms.dqn.dqn_tf_policy import PRIO_WEIGHTS, build_q_model, clip_gradients, compute_q_values, postprocess_nstep_and_prio
  from ray.rllib.models.action_dist import ActionDistribution
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.tf.tf_action_dist import Categorical
  from ray.rllib.models.torch.torch_action_dist import TorchCategorical
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.tf_mixins import LearningRateSchedule, TargetNetworkMixin
  from ray.rllib.policy.tf_policy_template import build_tf_policy
  from ray.rllib.utils.framework import try_import_tf
  from ray.rllib.utils.tf_utils import huber_loss
  from ray.rllib.utils.typing import AlgorithmConfigDict, ModelInputDict, TensorType
label_desc: Use image node_id 2 for calling a global function with example usage: r2d2_loss(policy, model, _, train_batch) and returns: policy, tf