class_docstr: Custom Experiment class for running distributed experiments.
class_docstr_tok: ["Custom","Experiment","class","for","running","distributed","experiments","."]
code: def evaluate_on_train_data(self):
      name = "eval_train"
      self._set_eval_dir_name(name)
      self._estimator.evaluate(
          self._train_spec.input_fn,
          steps=self._eval_spec.steps,
          hooks=self._eval_spec.hooks,
          name=name,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import contextlib
  import json
  import os
  import random
  import numpy
  from tensor2tensor.utils import contrib
  from tensor2tensor.utils import decoding
  from tensor2tensor.utils import devices
  from tensor2tensor.utils import hparams_lib
  from tensor2tensor.utils import metrics_hook
  from tensor2tensor.utils import mlperf_log
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
  from tensorflow.core.protobuf import rewriter_config_pb2
  from tensorflow.python import debug
label_desc: Use image node_id 11 for calling the T2TExperiment obj's underlying member method code with example usage: obj.evaluate_on_train_data() without return types