docstr: Predict ratings for every user and item.
docstr_tok: ["Predict","ratings","for","every","user","and","item","."]
code: def predict_all(self):
      """Predict ratings for every user and item."""
      predictions = self.user_vecs.dot(self.item_vecs.T)
      predictions += (
          self.global_bias
          + self.user_bias[:, np.newaxis]
          + self.item_bias[np.newaxis, :]
      )
      return predictions
  
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 5 for calling the MatrixFactorization obj's underlying member method code with example usage: obj.predict_all() and returns: predictions