code: def _generate_sparse(
      data,
      sparse_containers=tuple(
          COO_CONTAINERS
          + CSC_CONTAINERS
          + CSR_CONTAINERS
          + DOK_CONTAINERS
          + LIL_CONTAINERS
      ),
      dtypes=(bool, int, np.int8, np.uint8, float, np.float32),
  ):
      return [
          sparse_container(data, dtype=dtype)
          for sparse_container in sparse_containers
          for dtype in dtypes
      ]
  
imports: from itertools import product
  import numpy
  import pytest
  from scipy.sparse import issparse
  from sklearn import config_context, datasets
  from sklearn.model_selection import ShuffleSplit
  from sklearn.svm import SVC
  from sklearn.utils._array_api import yield_namespace_device_dtype_combinations
  from sklearn.utils._testing import _array_api_for_tests, assert_allclose, assert_array_almost_equal, assert_array_equal
  from sklearn.utils.estimator_checks import _NotAnArray
  from sklearn.utils.fixes import COO_CONTAINERS, CSC_CONTAINERS, CSR_CONTAINERS, DOK_CONTAINERS, LIL_CONTAINERS
  from sklearn.utils.metaestimators import _safe_split
  from sklearn.utils.multiclass import _ovr_decision_function, check_classification_targets, class_distribution, is_multilabel, type_of_target, unique_labels
label_desc: Use image node_id 1 for calling a global function with example usage: _generate_sparse(data, sparse_containers, dtypes) and returns: unknown