code: def get_user_representations(self, features=None):
          
  
          self._check_initialized()
  
          if features is None:
              return self.user_biases, self.user_embeddings
  
          features = sp.csr_matrix(features, dtype=CYTHON_DTYPE)
  
          return features * self.user_biases, features * self.user_embeddings
docs: Get the latent representations for users given model and features.
  
          Arguments
          ---------
  
          features: np.float32 csr_matrix of shape [n_users, n_user_features], optional
               Each row contains that user's weights over features.
               An identity matrix will be used if not supplied.
  
          Returns
          -------
  
          (user_biases, user_embeddings):
                  (np.float32 array of shape n_users
                   np.float32 array of shape [n_users, num_components]
              Biases and latent representations for users.