code: def l0_ohe(
      input: torch.Tensor, target: torch.Tensor, reduction: str = "none"
  ) -> torch.Tensor:
      """
      Computes the L0 loss for a one-hot encoding representation.
  
      Parameters
      ----------
      input
          Input tensor.
      target
          Target tensor
      reduction
          Specifies the reduction to apply to the output: ``'none'`` | ``'mean'`` | ``'sum'``.
  
      Returns
      -------
      L0 loss.
      """
      # Order matters as the gradient of zeros will still flow if reversed order. Maybe consider clipping a bit higher?
      eps = 1e-7 / input.shape[1]
      loss = torch.maximum(
          target - input, eps + torch.zeros_like(input)
      ).sum(dim=1)
  
      if reduction == "none":
          return loss
  
      if reduction == "mean":
          return torch.mean(loss)
  
      if reduction == "sum":
          return torch.sum(loss)
  
      raise ValueError(f"Reduction {reduction} not implemented.")
  
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 2 for calling a global function with example usage: l0_ohe(input, target, reduction) and returns: loss, torch, torch