code: def sgd(self, X, training_indices):
      # to do: batch learning
      for u, i in training_indices:
          prediction = self.predict(u, i)
          e = X[u, i] - prediction  # error
  
          # Update biases
          self.user_bias[u] += self.learning_rate * (
              e - self.shrinkage_value * self.user_bias[u]
          )
          self.item_bias[i] += self.learning_rate * (
              e - self.shrinkage_value * self.item_bias[i]
          )
  
          # Update latent factors
          self.user_vecs[u, :] += self.learning_rate * (
              e * self.item_vecs[i, :]
              - self.shrinkage_value * self.user_vecs[u, :]
          )
          self.item_vecs[i, :] += self.learning_rate * (
              e * self.user_vecs[u, :]
              - self.shrinkage_value * self.item_vecs[i, :]
          )
  
file_path: fancyimpute/fancyimpute/matrix_factorization.py
imports: import numpy
  from sklearn.utils import check_array
  from .solver import Solver
  from .common import masked_mae
label_desc: Use image node_id 3 for calling the MatrixFactorization obj's underlying member method code with example usage: obj.sgd(X, training_indices) without return types