code: def test_l0_loss(reduction, backend):
      NUM_CLASSES = 5
      NUM_SAMPLES = 1000
  
      def generate_random_labels():
          y = np.random.randint(0, NUM_CLASSES, size=NUM_SAMPLES)
          y_ohe = np.zeros((NUM_SAMPLES, NUM_CLASSES), dtype=np.float32)
          y_ohe[np.arange(NUM_SAMPLES), y] = 1
          assert np.all(np.sum(y_ohe, axis=1) == 1)
          return y, y_ohe
  
      y1, y1_ohe = generate_random_labels()
      y2, y2_ohe = generate_random_labels()
  
      if backend == "tensorflow":
          from alibi.explainers.backends.tensorflow.cfrl_base import (
              to_tensor,
              to_numpy,
          )
          from alibi.explainers.backends.tensorflow.cfrl_tabular import (
              l0_ohe,
          )
  
          device = None
      else:
          import torch
          from alibi.explainers.backends.pytorch.cfrl_base import (
              to_tensor,
              to_numpy,
          )
          from alibi.explainers.backends.pytorch.cfrl_tabular import (
              l0_ohe,
          )
  
          device = torch.device("cpu")
  
      y1_ohe, y2_ohe = to_tensor(y1_ohe, device=device), to_tensor(
          y2_ohe, device=device
      )
      l0_ohe_loss = l0_ohe(y1_ohe, y2_ohe, reduction=reduction)
      l0_ohe_loss = to_numpy(l0_ohe_loss)
      l0_loss = y1 != y2
  
      if reduction == "none":
          assert_allclose(l0_loss, l0_ohe_loss, atol=1e-5)
      elif reduction == "sum":
          assert np.isclose(np.mean(l0_loss), l0_ohe_loss / NUM_SAMPLES)
      else:
          assert np.isclose(np.mean(l0_loss), l0_ohe_loss)
  
imports: from typing import Union, List
  import pytest
  from pytest_lazyfixture import lazy_fixture
  import numpy
  from numpy.testing import assert_allclose
  import tensorflow
  import tensorflow.keras
  from alibi.explainers import CounterfactualRLTabular
  from alibi.explainers.backends.cfrl_base import get_hard_distribution
  from alibi.explainers.backends.cfrl_tabular import get_he_preprocessor, split_ohe, get_numerical_conditional_vector, get_categorical_conditional_vector, get_statistics, get_conditional_vector, sample
label_desc: Use image node_id 10 for calling a global function with example usage: test_l0_loss(reduction, backend) and returns: y,  y_ohe