<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def get_item_representations(self, features=None):
 """ 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. """


        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