code: def map_action_to_triplet(
      action_tensor: torch.Tensor,
      cardinality: int = 5,
      vector_size: int = 5,
      add_bias: bool = True,
  ):
      """Maps a batch of actions to the batch of triplets that generated them.
  
      Args:
          action_tensor (torch.Tensor): Batch of actions.
          cardinality (int, optional): Cardinality of the action space.
          vector_size (int, optional): Size of the vector.
          add_bias (bool, optional): Whether to use bias.
      """
      # map the action to a triplet. The action is converted to a base 5
      # representation and then the three elements are extracted from it.
      # The action has shape (bs, n_steps) and it contains the token for
      # recreating u, v and w. The token is a number between 0 and n_logits.
      action_shape = action_tensor.shape
      action_tensor = action_tensor.reshape(-1)
      if add_bias:
          bias = cardinality // 2
      else:
          bias = 0
      triplets = torch.stack(
          [
              _single_action_to_triplet(
                  action_tensor[idx],
                  cardinality,
                  vector_size,
                  bias,
                  action_tensor.device,
              )
              for idx in range(len(action_tensor))
          ]
      )
      final_size = triplets.shape[-1]
      return triplets.reshape((*action_shape, final_size))
  
imports: from typing import Tuple
  import torch
label_desc: Use image node_id 4 for calling a global function with example usage: map_action_to_triplet(action_tensor, cardinality, vector_size, add_bias) and returns: triplets