class_docstr: base class for classfication 
docstr: train and eval for one epoch 
docstr_tok: ["train","and","eval","for","one","epoch"]
code: def train_and_eval_one_epoch(self, nn, train_spec, eval_spec):
      """train and eval for one epoch"""
      (
          eval_result,
          export_result,
      ) = tf.estimator.train_and_evaluate(  # pylint: disable=no-member
          nn, train_spec, eval_spec
      )
      logging.info("Export result:{}".format(export_result))
      self.log_eval_metrics(eval_result)
  
imports: import os
  import re
  import functools
  import collections
  from absl import logging
  import delta.compat
  from tensorflow.python import debug
  from tensorflow.python.estimator.canned import metric_keys
  from tensorboard.plugins.pr_curve import summary
  from delta import utils
  from delta.utils.hparam import HParams
  from delta.utils import metrics
  from delta.utils import summary
  from delta.utils.register import registers
  from delta.utils.solver.base_solver import ABCEstimatorSolver
label_desc: Use image node_id 16 for calling the EstimatorSolver obj's underlying member method code with example usage: obj.train_and_eval_one_epoch(nn, train_spec, eval_spec) without return types