class_docstr: Custom Experiment class for running distributed experiments.
class_docstr_tok: ["Custom","Experiment","class","for","running","distributed","experiments","."]
docstr: Decode from dataset on new checkpoint.
code: def continuous_decode_on_eval_data(self):
      """Decode from dataset on new checkpoint."""
      if self._hparams.mlperf_mode:
          ckpt_generator = next_undecoded_checkpoint(
              self._hparams.model_dir,
              self._decode_hparams.decode_timeout_mins,
          )
      else:
          ckpt_generator = next_checkpoint(
              self._hparams.model_dir,
              self._decode_hparams.decode_timeout_mins,
          )
  
      for ckpt in ckpt_generator:
          current_step = decoding.get_step_from_ckpt_path(ckpt)
          tf.logging.info("Decoding step %d" % current_step)
          # Skip checkpoint 0.
          if current_step == 0:
              continue
          # Decode the latest checkpoint by default.
          checkpoint_path = None
          if self._hparams.mlperf_mode:
              self._decode_hparams.mlperf_decode_step = current_step
              checkpoint_path = ckpt
  
          mlperf_log.transformer_print(key=mlperf_log.EVAL_START)
          self.decode(
              dataset_split=tf_estimator.ModeKeys.EVAL,
              checkpoint_path=checkpoint_path,
          )
          d_hparams = self._decode_hparams
          if self._hparams.mlperf_mode and d_hparams.mlperf_success:
              mlperf_log.transformer_print(
                  key=mlperf_log.RUN_STOP, value={"success": "true"}
              )
              break
  
      d_hparams = self._decode_hparams
      if self._hparams.mlperf_mode and not d_hparams.mlperf_success:
          mlperf_log.transformer_print(
              key=mlperf_log.RUN_STOP, value={"success": "false"}
          )
  
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 19 for calling the T2TExperiment obj's underlying member method code with example usage: obj.continuous_decode_on_eval_data() without return types