code: def test_min_max(
      dtype,
      axis,
      sparse_format,
      missing_values,
      min_func,
      max_func,
      ignore_nan,
      large_indices,
  ):
      X = np.array(
          [
              [0, 3, 0],
              [2, -1, missing_values],
              [0, 0, 0],
              [9, missing_values, 7],
              [4, 0, 5],
          ],
          dtype=dtype,
      )
      X_sparse = sparse_format(X)
  
      if large_indices:
          X_sparse.indices = X_sparse.indices.astype("int64")
          X_sparse.indptr = X_sparse.indptr.astype("int64")
  
      mins_sparse, maxs_sparse = min_max_axis(
          X_sparse, axis=axis, ignore_nan=ignore_nan
      )
      assert_array_equal(mins_sparse, min_func(X, axis=axis))
      assert_array_equal(maxs_sparse, max_func(X, axis=axis))
  
imports: import numpy
  import pytest
  import scipy.sparse
  from numpy.random import RandomState
  from numpy.testing import assert_array_almost_equal, assert_array_equal
  from scipy import linalg
  from sklearn.datasets import make_classification
  from sklearn.utils._testing import assert_allclose
  from sklearn.utils.fixes import CSC_CONTAINERS, CSR_CONTAINERS, LIL_CONTAINERS
  from sklearn.utils.sparsefuncs import _implicit_column_offset, count_nonzero, csc_median_axis_0, incr_mean_variance_axis, inplace_column_scale, inplace_row_scale, inplace_swap_column, inplace_swap_row, mean_variance_axis, min_max_axis
  from sklearn.utils.sparsefuncs_fast import assign_rows_csr, csr_row_norms, inplace_csr_row_normalize_l1, inplace_csr_row_normalize_l2
label_desc: Use image node_id 18 for calling a global function with example usage: test_min_max(dtype, axis, sparse_format, missing_values, min_func, max_func, ignore_nan, large_indices) without return types