code: def update_factors(self, item, i):
      c, e = 0, 0
  
      for user in self.train_set["users_viewed_item"].get(item, []):
          u = self.user_to_user_id[user]
          rui = self._predict(u, i)
          error = self.train_set["feedback"][user][item] - rui
  
          b = np.array(self.p[u])
  
          # update factors
          self.p[u] += self.learn_rate * (
              error * self.q[i] - self.delta * self.p[u]
          )
          self.q[i] += self.learn_rate * (
              error * b - self.delta * self.q[i]
          )
          self.b[u] += self.learn_rate * (
              error - self.delta * self.b[u]
          )
          self.c[i] += self.learn_rate * (
              error - self.delta * self.c[i]
          )
          c += 1
          e += error**2
  
      return c, e
  
file_path: CaseRecommender/caserec/recommenders/rating_prediction/item_nsvd1.py
imports: import numpy
  from caserec.recommenders.rating_prediction.base_nsvd1 import BaseNSVD1
  from caserec.utils.extra_functions import timed
  from caserec.utils.process_data import ReadFile
label_desc: Use image node_id 4 for calling the ItemNSVD1 obj's underlying member method code with example usage: obj.update_factors(item, i) and returns: c,  e