def get_user_representations(self, features=None):
self._check_initialized()<EOL>if features is None:<EOL><INDENT>return self.user_biases, self.user_embeddings<EOL><DEDENT>features = sp.csr_matrix(features, dtype=CYTHON_DTYPE)<EOL>return features * self.user_biases, features * self.user_embeddings<EOL>
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.