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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 policy_and_value_opt_step(i, opt_state, opt_update, policy_and_value_net_apply, old_params, padded_observations, padded_actions, padded_rewards, reward_mask, c1=1.0, c2=0.01, gamma=0.99, lambda_=0.95, epsilon=0.1):
 """Policy and Value optimizer step."""

  # Combined loss function given the new params.
  def policy_and_value_loss(params):
    """Returns the combined loss given just parameters."""
    (loss, _, _, _) = combined_loss(
        params,
        old_params,
        policy_and_value_net_apply,
        padded_observations,
        padded_actions,
        padded_rewards,
        reward_mask,
        c1=c1,
        c2=c2,
        gamma=gamma,
        lambda_=lambda_,
        epsilon=epsilon)
    return loss

  new_params = trax_opt.get_params(opt_state)
  g = grad(policy_and_value_loss)(new_params)
  return opt_update(i, g, opt_state)