code: def test_incr_mean_variance_no_new_n():
      # check the behaviour when we update the variance with an empty matrix
      axis = 0
      X1 = sp.random(5, 1, density=0.8, random_state=0).tocsr()
      X2 = sp.random(0, 1, density=0.8, random_state=0).tocsr()
      last_mean, last_var = np.zeros(X1.shape[1]), np.zeros(X1.shape[1])
      last_n = np.zeros(X1.shape[1], dtype=np.int64)
      last_mean, last_var, last_n = incr_mean_variance_axis(
          X1,
          axis=axis,
          last_mean=last_mean,
          last_var=last_var,
          last_n=last_n,
      )
      # update statistic with a column which should ignored
      updated_mean, updated_var, updated_n = incr_mean_variance_axis(
          X2,
          axis=axis,
          last_mean=last_mean,
          last_var=last_var,
          last_n=last_n,
      )
      assert_allclose(updated_mean, last_mean)
      assert_allclose(updated_var, last_var)
      assert_allclose(updated_n, last_n)
  
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 9 for calling a global function with example usage: test_incr_mean_variance_no_new_n() without return types