code: def l1_loss(
      input: torch.Tensor, target: torch.Tensor, reduction: str = "none"
  ) -> torch.Tensor:
      """
      Computes L1 loss.
  
      Parameters
      ----------
      input
          Input tensor.
      target
          Target tensor.
      reduction
          Specifies the reduction to apply to the output: ``'none'`` | ``'mean'`` | ``'sum'``.
  
      Returns
      -------
      L1 loss.
      """
      return F.l1_loss(input=input, target=target, reduction=reduction)
  
file_path: alibi/alibi/explainers/backends/pytorch/cfrl_tabular.py
imports: from typing import List, Dict
  import torch
  import torch.nn.functional
  from alibi.explainers.backends.cfrl_tabular import split_ohe, generate_condition
  from alibi.explainers.backends.pytorch.cfrl_base import get_actor, get_critic, get_optimizer, data_generator, encode, decode, generate_cf, update_actor_critic, add_noise, to_numpy, to_tensor, set_seed, save_model, load_model
label_desc: Use image node_id 3 for calling a global function with example usage: l1_loss(input, target, reduction) and returns: F