code: def test_srl_eval_reports_zero_error_for_perfect_model(
      self, precision
  ):
      vocab_size = 100
      n_steps = 5
  
      multibonacci_modulo = make_multibonacci_modulo(
          2 * precision, vocab_size
      )
      space = gym.spaces.MultiDiscrete(nvec=([vocab_size] * precision))
      (obs, act) = generate_trajectory(
          multibonacci_modulo, space, n_steps
      )
      eval_task = make_singleton_eval_task(obs, act)
      seq_model = functools.partial(
          tl_test_utils.MockTransformerLM,
          sequence_fn=multibonacci_modulo,
      )
      serialized_model = make_serialized_model(
          seq_model, space, vocab_size
      )
      callback = callbacks.SerializedModelEvaluation(
          loop=None,
          eval_task=eval_task,
          model=serialized_model,
          context_lengths=(1,),
          horizon_lengths=(4,),
          accelerate_model=False,
      )
      metrics = callback.evaluate(weights=None)
      error = next(
          value
          for (name, value) in metrics.items()
          if "pred_error" in name
      )
      assert error == 0
  
imports: import functools
  import io
  from unittest import mock
  from absl.testing import absltest
  from absl.testing import parameterized
  import gym
  import numpy
  from trax import models
  from trax import test_utils
  from trax.data import inputs
  from trax.layers import test_utils
  from trax.rl import serialization_utils
  from trax.rl import space_serializer
  from trax.supervised import callbacks
  from trax.supervised import lr_schedules
  from trax.supervised import trainer_lib
  from trax.supervised import training
label_desc: Use image node_id 4 for calling the CallbacksTest obj's underlying member method code with example usage: obj.test_srl_eval_reports_zero_error_for_perfect_model(precision) without return types