code: def testEvaluatePerfectModel(self):
      checkpoint_dir = os.path.join(
          self.get_temp_dir(), "evaluate_perfect_model_once"
      )
  
      # Train a Model to completion:
      self._train_model(checkpoint_dir, num_steps=300)
  
      # Run
      inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)
      labels = constant_op.constant(self._labels, dtype=dtypes.float32)
      logits = logistic_classifier(inputs)
      predictions = math_ops.round(logits)
  
      accuracy, update_op = metrics_module.accuracy(labels, predictions)
  
      checkpoint_path = saver.latest_checkpoint(checkpoint_dir)
  
      final_ops_values = evaluation._evaluate_once(
          checkpoint_path=checkpoint_path,
          eval_ops=update_op,
          final_ops={"accuracy": (accuracy, update_op)},
          hooks=[
              evaluation._StopAfterNEvalsHook(1),
          ],
      )
      self.assertGreater(final_ops_values["accuracy"], 0.99)
  
imports: import os
  import numpy
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import random_seed
  from tensorflow.python.layers import layers
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import metrics
  from tensorflow.python.ops import state_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.ops.losses import losses
  from tensorflow.python.platform import test
  from tensorflow.python.training import basic_session_run_hooks
  from tensorflow.python.training import evaluation
  from tensorflow.python.training import gradient_descent
  from tensorflow.python.training import monitored_session
  from tensorflow.python.training import saver
  from tensorflow.python.training import training
label_desc: Use image node_id 3 for calling the EvaluateOnceTest obj's underlying member method code with example usage: obj.testEvaluatePerfectModel() without return types