code: def test_evaluate_no_metric_fn_falls_back_to_adanet_losses(
      self, input_fn, steps, adanet_losses, want_adanet_losses
  ):
      with context.graph_mode():
          adanet_losses = adanet_losses(input_fn)
          metrics = [
              {"adanet_loss": tf_compat.v1.metrics.mean(loss)}
              for loss in adanet_losses
          ]
          with self.test_session() as sess:
              evaluator = Evaluator(input_fn=input_fn, steps=steps)
              adanet_losses = evaluator.evaluate(
                  sess, ensemble_metrics=metrics
              )
              self.assertEqual(want_adanet_losses, adanet_losses)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  from adanet import tf_compat
  from adanet.core.evaluator import Evaluator
  import adanet.core.testing_utils
  import numpy
  import tensorflow.compat.v2
  from tensorflow.python.eager import context
  from tensorflow.python.framework import test_util
label_desc: Use image node_id 1 for calling the EvaluatorTest obj's underlying member method code with example usage: obj.test_evaluate_no_metric_fn_falls_back_to_adanet_losses(input_fn, steps, adanet_losses, want_adanet_losses) without return types