code: def make_env_and_model(
      bsuite_id: str, results_dir: str, overwrite: bool
  ) -> Tuple[dm_env.Environment, models.Model]:
      """Create environment and corresponding model (learned or simulator)."""
      raw_env = bsuite.load_from_id(bsuite_id)
      if FLAGS.simulator:
          model = simulator.Simulator(
              raw_env
          )  # pytype: disable=attribute-error
      else:
          model = mlp.MLPModel(
              specs.make_environment_spec(raw_env),
              replay_capacity=1000,
              batch_size=16,
              hidden_sizes=(50,),
          )
      environment = csv_logging.wrap_environment(
          raw_env, bsuite_id, results_dir, overwrite
      )
      environment = wrappers.SinglePrecisionWrapper(environment)
  
      return environment, model
  
file_path: acme/examples/bsuite/run_mcts.py
imports: from typing import Tuple
  from absl import app
  from absl import flags
  import acme
  from acme import specs
  from acme import wrappers
  from acme.agents.tf import mcts
  from acme.agents.tf.mcts import models
  from acme.agents.tf.mcts.models import mlp
  from acme.agents.tf.mcts.models import simulator
  from acme.tf import networks
  import bsuite
  from bsuite.logging import csv_logging
  import dm_env
  import sonnet
label_desc: Use image node_id 1 for calling a global function with example usage: make_env_and_model(bsuite_id, results_dir, overwrite) and returns: environment,  model