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