code: def centered_matrices(request):
      """Returns equivalent tuple[sp.linalg.LinearOperator, np.ndarray]."""
      sparse_container = request.param
  
      random_state = np.random.default_rng(42)
  
      X_sparse = sparse_container(
          sp.random(
              500,
              100,
              density=0.1,
              format="csr",
              random_state=random_state,
          )
      )
      X_dense = X_sparse.toarray()
      mu = np.asarray(X_sparse.mean(axis=0)).ravel()
  
      X_sparse_centered = _implicit_column_offset(X_sparse, mu)
      X_dense_centered = X_dense - mu
  
      return X_sparse_centered, X_dense_centered
  
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 24 for calling a global function with example usage: centered_matrices(request) and returns: X_sparse_centered,  X_dense_centered