code: def test_argkmin_classmode_strategy_consistent():
      rng = np.random.RandomState(1)
      X = rng.rand(100, 10)
      Y = rng.rand(100, 10)
      k = 5
      metric = "manhattan"
  
      weights = "uniform"
      Y_labels = rng.randint(low=0, high=10, size=100)
      unique_Y_labels = np.unique(Y_labels)
      results_X = ArgKminClassMode.compute(
          X=X,
          Y=Y,
          k=k,
          metric=metric,
          weights=weights,
          Y_labels=Y_labels,
          unique_Y_labels=unique_Y_labels,
          strategy="parallel_on_X",
      )
      results_Y = ArgKminClassMode.compute(
          X=X,
          Y=Y,
          k=k,
          metric=metric,
          weights=weights,
          Y_labels=Y_labels,
          unique_Y_labels=unique_Y_labels,
          strategy="parallel_on_Y",
      )
      assert_array_equal(results_X, results_Y)
  
imports: import itertools
  import re
  import warnings
  from functools import partial
  import numpy
  import pytest
  import threadpoolctl
  from scipy.spatial.distance import cdist
  from sklearn.metrics import euclidean_distances, pairwise_distances
  from sklearn.metrics._pairwise_distances_reduction import ArgKmin, ArgKminClassMode, BaseDistancesReductionDispatcher, RadiusNeighbors, RadiusNeighborsClassMode, sqeuclidean_row_norms
  from sklearn.utils._testing import assert_allclose, assert_array_equal, create_memmap_backed_data
  from sklearn.utils.fixes import CSR_CONTAINERS
label_desc: Use image node_id 22 for calling a global function with example usage: test_argkmin_classmode_strategy_consistent() without return types