code: def update_actor_critic(
      encoder: nn.Module,
      decoder: nn.Module,
      critic: nn.Module,
      actor: nn.Module,
      optimizer_critic: torch.optim.Optimizer,
      optimizer_actor: torch.optim.Optimizer,
      sparsity_loss: Callable,
      consistency_loss: Callable,
      coeff_sparsity: float,
      coeff_consistency: float,
      X: np.ndarray,
      X_cf: np.ndarray,
      Z: np.ndarray,
      Z_cf_tilde: np.ndarray,
      Y_m: np.ndarray,
      Y_t: np.ndarray,
      C: Optional[np.ndarray],
      R_tilde: np.ndarray,
      device: torch.device,
      **kwargs,
  ):
      """
      Training step. Updates actor and critic networks including additional losses.
  
      Parameters
      ----------
      encoder
          Pretrained encoder network.
      decoder
          Pretrained decoder network.
      critic
          Critic network.
      actor
          Actor network.
      optimizer_critic
          Critic's optimizer.
      optimizer_actor
          Actor's optimizer.
      sparsity_loss
          Sparsity loss function.
      consistency_loss
          Consistency loss function.
      coeff_sparsity
          Sparsity loss coefficient.
      coeff_consistency
          Consistency loss coefficient
      X
          Input array.
      X_cf
          Counterfactual array.
      Z
          Input embedding.
      Z_cf_tilde
          Noised counterfactual embedding.
      Y_m
          Input classification label.
      Y_t
          Target counterfactual classification label.
      C
          Conditional tensor.
      R_tilde
          Noised counterfactual reward.
      device
          Torch device object.
      **kwargs
          Other arguments. Not used.
  
      Returns
      -------
      Dictionary of losses.
      """
      # Set autoencoder to evaluation mode.
      encoder.eval()
      decoder.eval()
  
      # Set actor and critic to training mode.
      actor.train()
      critic.train()
  
      # Define dictionary of losses.
      losses: Dict[str, float] = dict()
  
      # Transform data to tensors and it device
      X = torch.tensor(X).float().to(device)  # type: ignore
      X_cf = torch.tensor(X_cf).float().to(device)  # type: ignore
      Z = torch.tensor(Z).float().to(device)  # type: ignore
      Z_cf_tilde = torch.tensor(Z_cf_tilde).float().to(device)  # type: ignore
      Y_m = torch.tensor(Y_m).float().to(device)  # type: ignore
      Y_t = torch.tensor(Y_t).float().to(device)  # type: ignore
      C = torch.tensor(C).float().to(device) if (C is not None) else None  # type: ignore
      R_tilde = torch.tensor(R_tilde).float().to(device)  # type: ignore
  
      # Define state by concatenating the input embedding, the classification label, the target label, and optionally
      # the conditional vector if exists.
      state = [Z, Y_m, Y_t] + ([C] if (C is not None) else [])
      state = torch.cat(state, dim=1).to(device)  # type: ignore
  
      # Define input for critic, compute q-values and append critic's loss.
      input_critic = torch.cat([state, Z_cf_tilde], dim=1).float()  # type: ignore
      output_critic = critic(input_critic).squeeze(1)
      loss_critic = F.mse_loss(output_critic, R_tilde)  # type: ignore
      losses.update({"critic_loss": loss_critic.item()})
  
      # Update critic by gradient step.
      optimizer_critic.zero_grad()
      loss_critic.backward()
      optimizer_critic.step()
  
      # Compute counterfactual embedding.
      Z_cf = actor(state)
  
      # Compute critic's output.
      critic.eval()
      input_critic = torch.cat([state, Z_cf], dim=1)  # type: ignore
      output_critic = critic(input_critic)
  
      # Compute actor's loss.
      loss_actor = -torch.mean(output_critic)
      losses.update({"actor_loss": loss_actor.item()})
  
      # Decode the output of the actor.
      X_hat_cf = decoder(Z_cf)
  
      # Compute sparsity losses.
      loss_sparsity = sparsity_loss(X_hat_cf, X)
      losses.update(loss_sparsity)
  
      # Add sparsity loss to the overall actor's loss.
      for key in loss_sparsity.keys():
          loss_actor += coeff_sparsity * loss_sparsity[key]
  
      # Compute consistency loss.
      Z_cf_tgt = encode(X=X_cf, encoder=encoder, device=device)  # type: ignore
      loss_consistency = consistency_loss(
          Z_cf_pred=Z_cf, Z_cf_tgt=Z_cf_tgt
      )
      losses.update(loss_consistency)
  
      # Add consistency loss to the overall actor loss.
      for key in loss_consistency.keys():
          loss_actor += coeff_consistency * loss_consistency[key]
  
      # Update by gradient descent.
      optimizer_actor.zero_grad()
      loss_actor.backward()
      optimizer_actor.step()
  
      # Return dictionary of losses for potential logging.
      return losses
  
imports: import os
  import random
  from typing import List, Dict, Callable, Union, Optional, TYPE_CHECKING
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.utils.data import DataLoader, Dataset
  from alibi.explainers.backends.cfrl_base import CounterfactualRLDataset
  from alibi.models.pytorch.actor_critic import Actor, Critic
label_desc: Use image node_id 12 for calling a global function with example usage: update_actor_critic(encoder, decoder, critic, actor, optimizer_critic, optimizer_actor, sparsity_loss, consistency_loss, coeff_sparsity, coeff_consistency, X, X_cf, Z, Z_cf_tilde, Y_m, Y_t, C, R_tilde, device) and returns: losses