code: def evaluate_model_init(self, model, features, mode, params):
      source = self.get_evaluate_from_input(features, params)
      _, loss, metric_name, metric = model(source)
      tf.train.get_or_create_global_step()
      tensor_to_log = {"loss": loss, metric_name: metric}
      hooks = []
      hooks.append(
          tf.train.LoggingTensorHook(tensor_to_log, every_n_iter=1)
      )
      spec = tf.estimator.EstimatorSpec(
          mode=mode, loss=loss, evaluation_hooks=hooks
      )
      return spec
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import json
  import numpy
  import tensorflow
  import tf_euler
label_desc: Use image node_id 7 for calling the BaseEstimator obj's underlying member method code with example usage: obj.evaluate_model_init(model, features, mode, params) and returns: spec