class_docstr: base class for classfication 
docstr: train for one epoch 
docstr_tok: ["train","for","one","epoch"]
code: def train_one_epoch(
      self, nn, steps=None
  ):  # pylint: disable=invalid-name
      """train for one epoch"""
      mode = utils.TRAIN
      tfconf = self.config["solver"]["run_config"]
      nn.train(
          input_fn=self.input_fn(mode),
          steps=steps,
          hooks=[tf_debug.LocalCLIDebugHook()]
          if tfconf["debug"]
          else None,
      )
  
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 12 for calling the EstimatorSolver obj's underlying member method code with example usage: obj.train_one_epoch(nn, steps) without return types