code: def make_singleton_eval_task(observations, actions):
      """Creates an EvalTask with just one example."""
      mask = np.ones(observations.shape[:2])
  
      def data():
          while True:
              yield (observations, actions, observations, mask)
  
      return training.EvalTask(
          labeled_data=data(),
          metrics=[],
      )
  
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 a global function with example usage: make_singleton_eval_task(observations, actions) and returns: training