How to use the mesa.time.RandomActivation function in Mesa

def __init__(self, agent_lifetime=1, n_agents=10):
        super().__init__()

        self.agent_lifetime = agent_lifetime
        self.n_agents = n_agents

        # keep track of the the remaining life of an agent and
        # how many ticks it has seen
        self.datacollector = DataCollector(
            agent_reporters={"remaining_life": lambda a: a.remaining_life,
                             "steps": lambda a: a.steps})

        self.current_ID = 0
        self.schedule = RandomActivation(self)

        for _ in range(n_agents):
            self.schedule.add(FiniteLifeAgent(self.next_id(),
                                              self.agent_lifetime,
                                              self))

def __init__(self, N=100, width=10, height=10):
        self.num_agents = N
        self.grid = MultiGrid(height, width, True)
        self.schedule = RandomActivation(self)
        self.datacollector = DataCollector(
            model_reporters={"Gini": compute_gini},
            agent_reporters={"Wealth": "wealth"}
        )
        # Create agents
        for i in range(self.num_agents):
            a = MoneyAgent(i, self)
            self.schedule.add(a)
            # Add the agent to a random grid cell
            x = random.randrange(self.grid.width)
            y = random.randrange(self.grid.height)
            self.grid.place_agent(a, (x, y))

        self.running = True
        self.datacollector.collect(self)

from mesa import Model
from mesa.time import BaseScheduler, RandomActivation, SimultaneousActivation
from mesa.space import SingleGrid
from mesa.datacollection import DataCollector

from .agent import PDAgent


class PdGrid(Model):
    ''' Model class for iterated, spatial prisoner's dilemma model. '''

    schedule_types = {"Sequential": BaseScheduler,
                      "Random": RandomActivation,
                      "Simultaneous": SimultaneousActivation}

    # This dictionary holds the payoff for this agent,
    # keyed on: (my_move, other_move)

    payoff = {("C", "C"): 1,
              ("C", "D"): 0,
              ("D", "C"): 1.6,
              ("D", "D"): 0}

    def __init__(self, height=50, width=50, schedule_type="Random", payoffs=None):
        '''
        Create a new Spatial Prisoners' Dilemma Model.

        Args:
            height, width: Grid size. There will be one agent per grid cell.

def __init__(self, num_nodes=10, avg_node_degree=3, initial_outbreak_size=1, virus_spread_chance=0.4,
                virus_check_frequency=0.4, recovery_chance=0.3, gain_resistance_chance=0.5):

        self.num_nodes = num_nodes
        prob = avg_node_degree / self.num_nodes
        self.G = nx.erdos_renyi_graph(n=self.num_nodes, p=prob)
        self.grid = NetworkGrid(self.G)
        self.schedule = RandomActivation(self)
        self.initial_outbreak_size = initial_outbreak_size if initial_outbreak_size <= num_nodes else num_nodes
        self.virus_spread_chance = virus_spread_chance
        self.virus_check_frequency = virus_check_frequency
        self.recovery_chance = recovery_chance
        self.gain_resistance_chance = gain_resistance_chance

        self.datacollector = DataCollector({"Infected": number_infected,
                                            "Susceptible": number_susceptible,
                                            "Resistant": number_resistant})

        # Create agents
        for i, node in enumerate(self.G.nodes()):
            a = VirusAgent(i, self, State.SUSCEPTIBLE, self.virus_spread_chance, self.virus_check_frequency,
                           self.recovery_chance, self.gain_resistance_chance)
            self.schedule.add(a)
            # Add the agent to the node

Create a new Flockers model.

        Args:
            population: Number of Boids
            width, height: Size of the space.
            speed: How fast should the Boids move.
            vision: How far around should each Boid look for its neighbors
            separation: What's the minimum distance each Boid will attempt to
                    keep from any other
            cohere, separate, match: factors for the relative importance of
                    the three drives.        '''
        self.population = population
        self.vision = vision
        self.speed = speed
        self.separation = separation
        self.schedule = RandomActivation(self)
        self.space = ContinuousSpace(width, height, True)
        self.factors = dict(cohere=cohere, separate=separate, match=match)
        self.make_agents()
        self.running = True

import random
from collections import defaultdict

from mesa.time import RandomActivation


class RandomActivationByBreed(RandomActivation):
    '''
    A scheduler which activates each type of agent once per step, in random
    order, with the order reshuffled every step.

    This is equivalent to the NetLogo 'ask breed...' and is generally the
    default behavior for an ABM.

    Assumes that all agents have a step() method.
    '''
    agents_by_breed = defaultdict(list)

    def __init__(self, model):
        super().__init__(model)
        self.agents_by_breed = defaultdict(list)

    def add(self, agent):

def __init__(self, height=grid_h, width=grid_w, init_people=2, rich_threshold=10,
                 reserve_percent=50,):
        self.height = height
        self.width = width
        self.init_people = init_people
        self.schedule = RandomActivation(self)
        self.grid = MultiGrid(self.width, self.height, torus=True)
        # rich_threshold is the amount of savings a person needs to be considered "rich"
        self.rich_threshold = rich_threshold
        self.reserve_percent = reserve_percent
        # see datacollector functions above
        self.datacollector = DataCollector(model_reporters={
                                           "Rich": get_num_rich_agents,
                                           "Poor": get_num_poor_agents,
                                           "Middle Class": get_num_mid_agents,
                                           "Savings": get_total_savings,
                                           "Wallets": get_total_wallets,
                                           "Money": get_total_money,
                                           "Loans": get_total_loans},
                                           agent_reporters={
                                           "Wealth": lambda x: x.wealth})

def __init__(self, N=2, width=20, height=10):
        self.N = N    # num of agents
        self.headings = ((1, 0), (0, 1), (-1, 0), (0, -1))  # tuples are fast
        self.grid = SingleGrid(width, height, torus=False)
        self.schedule = RandomActivation(self)
        self.make_walker_agents()
        self.running = True

def __init__(self, num_agents=7, num_nodes=10):

        self.num_agents = num_agents
        self.num_nodes = num_nodes if num_nodes >= self.num_agents else self.num_agents
        self.G = nx.erdos_renyi_graph(n=self.num_nodes, p=0.5)
        self.grid = NetworkGrid(self.G)
        self.schedule = RandomActivation(self)
        self.datacollector = DataCollector(
            model_reporters={"Gini": compute_gini},
            agent_reporters={"Wealth": lambda _: _.wealth}
        )

        list_of_random_nodes = random.sample(self.G.nodes(), self.num_agents)

        # Create agents
        for i in range(self.num_agents):
            a = MoneyAgent(i, self)
            self.schedule.add(a)
            # Add the agent to a random node
            self.grid.place_agent(a, list_of_random_nodes[i])

        self.running = True
        self.datacollector.collect(self)

def __init__(self, height=grid_h, width=grid_w, init_people=2, rich_threshold=10,
                 reserve_percent=50,):
        self.uid = next(self.id_gen)
        self.height = height
        self.width = width
        self.init_people = init_people
        self.schedule = RandomActivation(self)
        self.grid = MultiGrid(self.width, self.height, torus=True)
        # rich_threshold is the amount of savings a person needs to be considered "rich"
        self.rich_threshold = rich_threshold
        self.reserve_percent = reserve_percent
        # see datacollector functions above
        self.datacollector = DataCollector(model_reporters={
                                           "Rich": get_num_rich_agents,
                                           "Poor": get_num_poor_agents,
                                           "Middle Class": get_num_mid_agents,
                                           "Savings": get_total_savings,
                                           "Wallets": get_total_wallets,
                                           "Money": get_total_money,
                                           "Loans": get_total_loans,
                                           "Model Params": track_params,
                                           "Run": track_run},
                                           agent_reporters={