How to use the mathy.agents.a3c.A3CAgent function in mathy

def mathy_load_a3c(env_name: str, gym_env: MathyGymEnv, model: str):
    import tensorflow as tf

    global __agent
    if __agent is None:
        import os

        os.environ["TF_CPP_MIN_LOG_LEVEL"] = "5"
        tf.compat.v1.logging.set_verbosity("CRITICAL")
        if model is None:
            raise ValueError("model is none, must be specified")
        args = BaseConfig(model_dir=model)
        __agent = A3CAgent(args)

from datetime import datetime
from typing import List, Optional

import gym
import matplotlib.pyplot as plt
import numpy as np
import plac
from tqdm import trange

from mathy.agents.a3c import A3CAgent, BaseConfig
from mathy.agents.mcts import MCTS
from mathy.env.gym import MathyGymEnv

__mcts: Optional[MCTS] = None
__agent: Optional[A3CAgent] = None


def mathy_load_a3c(env_name: str, gym_env: MathyGymEnv, model: str):
    import tensorflow as tf

    global __agent
    if __agent is None:
        import os

        os.environ["TF_CPP_MIN_LOG_LEVEL"] = "5"
        tf.compat.v1.logging.set_verbosity("CRITICAL")
        if model is None:
            raise ValueError("model is none, must be specified")
        args = BaseConfig(model_dir=model)
        __agent = A3CAgent(args)

model_folder = tempfile.mkdtemp()
setup_tf_env()

args = A3CConfig(
    max_eps=3,
    verbose=True,
    topics=["poly"],
    model_dir=model_folder,
    update_gradients_every=4,
    num_workers=1,
    units=4,
    embedding_units=4,
    lstm_units=4,
    print_training=True,
)
instance = A3CAgent(args)
instance.train()
# Load the model back in
model_two = get_or_create_policy_model(
    args=args, predictions=PolySimplify().action_size, is_main=True
)
# Comment this out to keep your model
shutil.rmtree(model_folder)

model_folder = tempfile.mkdtemp()
setup_tf_env()

args = A3CConfig(
    profile=True,
    max_eps=2,
    verbose=True,
    mcts_sims=1,
    action_strategy="mcts_worker_0",
    topics=["poly-grouping"],
    model_dir=model_folder,
    num_workers=2,
    print_training=True,
)
A3CAgent(args).train()

assert os.path.isfile(os.path.join(args.model_dir, "worker_0.profile"))
assert os.path.isfile(os.path.join(args.model_dir, "worker_1.profile"))

# Comment this out to keep your model
shutil.rmtree(model_folder)

import shutil
import tempfile

model_folder = tempfile.mkdtemp()
setup_tf_env()

args = A3CConfig(
    action_strategy="mcts_worker_n",
    max_eps=1,
    verbose=True,
    topics=["poly-combine"],
    model_dir=model_folder,
    num_workers=2,
    print_training=True,
)
A3CAgent(args).train()
# Comment this out to keep your model
shutil.rmtree(model_folder)

import shutil
import tempfile

model_folder = tempfile.mkdtemp()
setup_tf_env()

args = A3CConfig(
    max_eps=1,
    verbose=True,
    action_strategy="mcts_worker_0",
    topics=["poly-combine"],
    model_dir=model_folder,
    num_workers=2,
    print_training=True,
)
A3CAgent(args).train()
# Comment this out to keep your model
shutil.rmtree(model_folder)