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