  Here is the Python code based on the given instructions:
```
import os
import numpy as np
from acme import Environment, NetworkFactory, ExperimentConfig

# Define flags for configuring the experiment
environment_name = 'CartPole'
num_demonstrations = 1000
learning_steps = 10000
batch_size = 32
learning_rate = 0.001
dropout_rate = 0.5
network_parameters = {'num_layers': 2, 'num_units': 64, 'activation': 'relu'}

# Define functions to create a demonstration dataset factory, an environment factory, and a network factory
def create_demonstration_dataset_factory(environment_name, num_demonstrations):
    def factory():
        environment = Environment(environment_name)
        demonstrations = []
        for i in range(num_demonstrations):
            state = environment.reset()
            action = environment.action_space.sample()
            next_state, reward, done, _ = environment.step(action)
            demonstrations.append((state, action, next_state, reward, done))
        return demonstrations
    return factory

def create_environment_factory(environment_name):
    def factory():
        return Environment(environment_name)
    return factory

def create_network_factory(network_parameters):
    def factory():
        return NetworkFactory(network_parameters)
    return factory

# Define a function to build the experiment configuration
def build_experiment_config(environment_factory, network_factory, demonstration_dataset_factory, learning_steps, batch_size, learning_rate, dropout_rate):
    experiment_config = ExperimentConfig(
        environment_factory=environment_factory,
        network_factory=network_factory,
        demonstration_dataset_factory=demonstration_dataset_factory,
        learning_steps=learning_steps,
        batch_size=batch_size,
        learning_rate=learning_rate,
        dropout_rate=dropout_rate
    )
    return experiment_config

# Define a main function that builds the experiment configuration and runs the experiment
def main():
    experiment_config = build_experiment_config(
        create_environment_factory(environment_name),
        create_network_factory(network_parameters),
        create_demonstration_dataset_factory(environment_name, num_demonstrations),
        learning_steps,
        batch_size,
        learning_rate,
        dropout_rate
    )
    run_experiment(experiment_config)

if __name__ == '__main__':
    main()
```
This code defines a behavioral cloning (BC) experiment on a specified environment using the Acme library. The experiment configuration is built using the `build_experiment_config` function, which takes the environment factory, network factory, demonstration dataset factory, learning steps, batch size, learning rate, and dropout rate as input. The `main` function builds the experiment configuration and runs the experiment using the `run_experiment` function. The experiment can be run in a distributed manner using the `make_distributed_experiment` function.