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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def get_user_representations(self, features=None):
 """ 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. """


        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