code: def test_compute_sample_weight_sparse(csc_container):
      """Check that we can compute weight for sparse `y`."""
      y = csc_container(np.asarray([[0], [1], [1]]))
      sample_weight = compute_sample_weight("balanced", y)
      assert_allclose(sample_weight, [1.5, 0.75, 0.75])
  
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 13 for calling a global function with example usage: test_compute_sample_weight_sparse(csc_container) without return types