code: def r2d2_loss(
      policy: Policy, model, _, train_batch: SampleBatch
  ) -> TensorType:
      """Constructs the loss for R2D2TorchPolicy.
  
      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.
      """
      target_model = policy.target_models[model]
      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,
          target_model,
          train_batch,
          state_batches=state_batches,
          seq_lens=train_batch.get(SampleBatch.SEQ_LENS),
          explore=False,
          is_training=True,
      )
  
      actions = train_batch[SampleBatch.ACTIONS].long()
      dones = train_batch[SampleBatch.TERMINATEDS].float()
      rewards = train_batch[SampleBatch.REWARDS]
      weights = train_batch[PRIO_WEIGHTS]
  
      B = state_batches[0].shape[0]
      T = q.shape[0] // B
  
      # Q scores for actions which we know were selected in the given state.
      one_hot_selection = F.one_hot(actions, policy.action_space.n)
      q_selected = torch.sum(
          torch.where(
              q > FLOAT_MIN, q, torch.tensor(0.0, device=q.device)
          )
          * one_hot_selection,
          1,
      )
  
      if config["double_q"]:
          best_actions = torch.argmax(q, dim=1)
      else:
          best_actions = torch.argmax(q_target, dim=1)
  
      best_actions_one_hot = F.one_hot(
          best_actions, policy.action_space.n
      )
      q_target_best = torch.sum(
          torch.where(
              q_target > FLOAT_MIN,
              q_target,
              torch.tensor(0.0, device=q_target.device),
          )
          * best_actions_one_hot,
          dim=1,
      )
  
      if config["num_atoms"] > 1:
          raise ValueError("Distributional R2D2 not supported yet!")
      else:
          q_target_best_masked_tp1 = (1.0 - dones) * torch.cat(
              [
                  q_target_best[1:],
                  torch.tensor([0.0], device=q_target_best.device),
              ]
          )
  
          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 = 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"
          ]
          if burn_in > 0 and burn_in < T:
              seq_mask[:, :burn_in] = False
  
          num_valid = torch.sum(seq_mask)
  
          def reduce_mean_valid(t):
              return torch.sum(t[seq_mask]) / num_valid
  
          # Make sure use the correct time indices:
          # Q(t) - [gamma * r + Q^(t+1)]
          q_selected = q_selected.reshape([B, T])[:, :-1]
          td_error = (
              q_selected - target.reshape([B, T])[:, :-1].detach()
          )
          td_error = td_error * seq_mask
          weights = weights.reshape([B, T])[:, :-1]
          total_loss = reduce_mean_valid(weights * huber_loss(td_error))
  
          # Store values for stats function in model (tower), such that for
          # multi-GPU, we do not override them during the parallel loss phase.
          model.tower_stats["total_loss"] = total_loss
          model.tower_stats["mean_q"] = reduce_mean_valid(q_selected)
          model.tower_stats["min_q"] = torch.min(q_selected)
          model.tower_stats["max_q"] = torch.max(q_selected)
          model.tower_stats["mean_td_error"] = reduce_mean_valid(
              td_error
          )
          # Store per time chunk (b/c we need only one mean
          # prioritized replay weight per stored sequence).
          model.tower_stats["td_error"] = torch.mean(td_error, dim=-1)
  
      return total_loss
  
imports: from typing import Dict, Tuple
  import gymnasium
  import ray
  from ray.rllib.algorithms.dqn.dqn_tf_policy import PRIO_WEIGHTS, postprocess_nstep_and_prio
  from ray.rllib.algorithms.dqn.dqn_torch_policy import adam_optimizer, build_q_model_and_distribution, compute_q_values
  from ray.rllib.algorithms.r2d2.r2d2_tf_policy import get_distribution_inputs_and_class
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.torch.torch_action_dist import TorchDistributionWrapper
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.policy_template import build_policy_class
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.torch_mixins import LearningRateSchedule, TargetNetworkMixin
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.torch_utils import FLOAT_MIN, apply_grad_clipping, concat_multi_gpu_td_errors, huber_loss, sequence_mask
  from ray.rllib.utils.typing import AlgorithmConfigDict, TensorType
label_desc: Use image node_id 2 for calling a global function with example usage: r2d2_loss(policy, model, _, train_batch) and returns: total_loss, unknown