  Here is the Python code based on the given instructions:
```
import acme
from acme import EnvironmentLoop
from acme import should_terminate
from acme import EnvironmentLoopTest
from acme import Environment
from acme import environment
from acme import OpenSpielEnvironmentLoop
from acme import make_environment
from acme import environment_factory
from acme import EnvironmentWrapper
from acme import EnvironmentSpec
from acme import DiscreteEnvironment
from acme import ContinuousEnvironment
from acme import _create_dummy_transitions
from acme import OpenSpielEnvironmentLoopTest
from acme import _slice_and_maybe_to_numpy
from acme import _create_embedding
from acme import _create_file
from acme import create_variables
from acme import NestedDiscreteEnvironment
from acme import _BaseDiscreteEnvironment

def create_agent(environment, dataset, batch_size, evaluation_period, num_demonstration_episodes, random_seed, learning_rate, discount, target_update_period, use_sarsa_target):
    # Create the environment loop
    environment_loop = EnvironmentLoop(environment, batch_size, evaluation_period, num_demonstration_episodes, random_seed, learning_rate, discount, target_update_period, use_sarsa_target)

    # Get the demonstrations dataset
    demonstrations = dataset.get_demonstrations()

    # Create the networks to optimize
    networks = create_variables(environment.observation_space, environment.action_space)

    # Create the learner
    learner = acme.Learner(networks, demonstrations, environment_loop)

    # Define the evaluator network
    evaluator_network = create_variables(environment.observation_space, environment.action_space)

    # Create the actor and the environment loop
    actor = acme.Actor(environment_loop, learner, evaluator_network)
    environment_loop.run(actor)

def add_next_action_extras(transition):
    # Add the next action extras to the transition
    next_action_extras = transition.next_action_extras
    next_action_extras.update({'next_action': transition.next_action})
    return next_action_extras

def main():
    # Create the environment
    environment = make_environment(environment_name)

    # Create the dataset
    dataset = acme.Dataset(environment, dataset_name)

    # Create the agent
    create_agent(environment, dataset, batch_size, evaluation_period, num_demonstration_episodes, random_seed, learning_rate, discount, target_update_period, use_sarsa_target)

if __name__ == '__main__':
    main()
```
Note that this code is just an example and may not work as is. It is important to test and validate the code before using it in a real-world scenario.