code: def test_incr_mean_variance_n_float():
      # check the behaviour when last_n is just a number
      axis = 0
      X = sp.random(5, 2, density=0.8, random_state=0).tocsr()
      last_mean, last_var = np.zeros(X.shape[1]), np.zeros(X.shape[1])
      last_n = 0
      _, _, new_n = incr_mean_variance_axis(
          X,
          axis=axis,
          last_mean=last_mean,
          last_var=last_var,
          last_n=last_n,
      )
      assert_allclose(new_n, np.full(X.shape[1], X.shape[0]))
  
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 10 for calling a global function with example usage: test_incr_mean_variance_n_float() without return types