code: def _train_implicit_row_lu(items, ratings, other, otOr):
      """
      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)
      """
      # we can optimize by only considering the nonzero entries of Cu-I
      # this means we only need the corresponding matrix columns
      M = other[items, :]
      # Compute M^T (C_u-I) M, restricted to these nonzero entries
      MMT = (M.T.copy() * ratings) @ M
      # Build the matrix for solving
      A = otOr + MMT
      # Compute RHS - only used columns (p_ui != 0) values needed
      # Cu is rates + 1 for the cols, so just trim Ot
      y = other.T[:, items] @ (ratings + 1.0)
      # and solve
      _dposv(A, y, True)
  
      return y
  
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 11 for calling a global function with example usage: _train_implicit_row_lu(items, ratings, other, otOr) and returns: y