def METHOD_NAME(workload: spec.Workload,
                         model_params: spec.ParameterContainer,
                         model_state: spec.ModelAuxiliaryState,
                         hyperparameters: spec.Hyperparameters,
                         rng: spec.RandomState) -> spec.OptimizerState:
  del model_params
  del model_state
  del rng
  params_zeros_like = jax.tree_map(lambda s: jnp.zeros(s.shape_tuple),
                                   workload.param_shapes)
  opt_init_fn, opt_update_fn = optimizer(hyperparameters,
                                         workload.num_train_examples)
  optimizer_state = opt_init_fn(params_zeros_like)
  return jax_utils.replicate(optimizer_state), opt_update_fn