code: def test_compute_sample_weight_more_than_32():
      # Non-regression smoke test for #12146
      y = np.arange(50)  # more than 32 distinct classes
      indices = np.arange(50)  # use subsampling
      weight = compute_sample_weight("balanced", y, indices=indices)
      assert_array_almost_equal(weight, np.ones(y.shape[0]))
  
imports: import numpy
  import pytest
  from numpy.testing import assert_allclose
  from sklearn.datasets import make_blobs
  from sklearn.linear_model import LogisticRegression
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.utils._testing import assert_almost_equal, assert_array_almost_equal
  from sklearn.utils.class_weight import compute_class_weight, compute_sample_weight
  from sklearn.utils.fixes import CSC_CONTAINERS
label_desc: Use image node_id 11 for calling a global function with example usage: test_compute_sample_weight_more_than_32() without return types