code: def _mean_centered(ui_mat):
      """Subtract every rating values with mean value of the corresponding rows"""
      mean_arr = np.zeros(ui_mat.shape[0])
      for i in range(ui_mat.shape[0]):
          start_idx, end_idx = ui_mat.indptr[i : i + 2]
          mean_arr[i] = np.mean(ui_mat.data[start_idx:end_idx])
          row_data = ui_mat.data[start_idx:end_idx]
          row_data -= mean_arr[i]
          row_data[row_data == 0] = EPS
          ui_mat.data[start_idx:end_idx] = row_data
  
      return ui_mat, mean_arr
  
imports: import multiprocessing
  import numpy
  from scipy.sparse import csr_matrix, coo_matrix
  from ..recommender import Recommender
  from ...exception import ScoreException
  from ...utils import get_rng
  from ...utils.fast_sparse_funcs import inplace_csr_row_normalize_l2
  from .similarity import compute_similarity, compute_score, compute_score_single
label_desc: Use image node_id 1 for calling a global function with example usage: _mean_centered(ui_mat) and returns: ui_mat,  mean_arr