code: def _train_bias_row_lu(items, ratings, other, reg):
      """
      Args:
          items(np.ndarray[i64]): the item IDs the user has rated
          ratings(np.ndarray): the user's (normalized) ratings for those items
          other(np.ndarray): the item-feature matrix
          reg(float): the regularization term
      Returns:
          np.ndarray: the user-feature vector (equivalent to V in the current LU code)
      """
      M = other[items, :]
      nf = other.shape[1]
      regI = np.identity(nf) * reg
      MMT = M.T @ M
      A = MMT + regI * len(items)
  
      V = M.T @ ratings
      _dposv(A, V, True)
  
      return V
  
imports: import logging
  from collections import namedtuple
  import numpy
  from numba import njit, prange
  from csr import CSR
  from seedbank import numpy_rng
  from .bias import Bias
  from .mf_common import MFPredictor
  from ..data import sparse_ratings
  from ..None import util
  from ..math.solve import _dposv
label_desc: Use image node_id 5 for calling a global function with example usage: _train_bias_row_lu(items, ratings, other, reg) and returns: V