class_docstr: Occasionally samples predictions from the training run and prints them.
  
  Params:
    every_n_secs: Sample predictions every N seconds.
      If set, `every_n_steps` must be None.
    every_n_steps: Sample predictions every N steps.
      If set, `every_n_secs` must be None.
    sample_dir: Optional, a directory to write samples to.
    delimiter: Join tokens on this delimiter. Defaults to space.
code: def after_run(self, _run_context, run_values):
      result_dict, step = run_values.results
      self._iter_count = step
  
      if not self._should_trigger:
          return None
  
      # Convert dict of lists to list of dicts
      result_dicts = [
          dict(zip(result_dict, t)) for t in zip(*result_dict.values())
      ]
  
      # Print results
      result_str = ""
      result_str += "Prediction followed by Target @ Step {}\n".format(
          step
      )
      result_str += ("=" * 100) + "\n"
      for result in result_dicts:
          target_len = result["target_len"]
          predicted_slice = result["predicted_tokens"][: target_len - 1]
          target_slice = result["target_words"][1:target_len]
          result_str += (
              self._target_delimiter.encode("utf-8")
              .join(predicted_slice)
              .decode("utf-8")
              + "\n"
          )
          result_str += (
              self._target_delimiter.encode("utf-8")
              .join(target_slice)
              .decode("utf-8")
              + "\n\n"
          )
      result_str += ("=" * 100) + "\n\n"
      tf.logging.info(result_str)
      if self._sample_dir:
          filepath = os.path.join(
              self._sample_dir, "samples_{:06d}.txt".format(step)
          )
          with gfile.GFile(filepath, "w") as file:
              file.write(result_str)
      self._timer.update_last_triggered_step(self._iter_count - 1)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import abc
  import os
  import numpy
  import six
  import yaml
  import tensorflow
  from tensorflow.python.training.basic_session_run_hooks import SecondOrStepTimer
  from tensorflow.python.training import session_manager
  from tensorflow.python.client import timeline
  from tensorflow import gfile
  from seq2seq.configurable import Configurable, abstractstaticmethod
  from seq2seq import graph_utils, global_vars
label_desc: Use image node_id 5 for calling the TrainSampleHook obj's underlying member method code with example usage: obj.after_run(_run_context, run_values) and returns: None