class_docstr: Used to incrementally build up a TensorFlow run.
  
  This is particularly useful for batching ops from multiple different
  policies in the multi-agent setting.
class_docstr_tok: ["Used","to","incrementally","build","up","a","TensorFlow","run",".","This","is","particularly","useful","for","batching","ops","from","multiple","different","policies","in","the","multi-agent","setting","."]
code: def get(self, to_fetch):
      if self._executed is None:
          try:
              self._executed = _run_timeline(
                  self.session,
                  self.fetches,
                  self.debug_name,
                  self.feed_dict,
                  os.environ.get("TF_TIMELINE_DIR"),
              )
          except Exception as e:
              logger.exception(
                  "Error fetching: {}, feed_dict={}".format(
                      self.fetches, self.feed_dict
                  )
              )
              raise e
      if isinstance(to_fetch, int):
          return self._executed[to_fetch]
      elif isinstance(to_fetch, list):
          return [self.get(x) for x in to_fetch]
      elif isinstance(to_fetch, tuple):
          return tuple(self.get(x) for x in to_fetch)
      else:
          raise ValueError(
              "Unsupported fetch type: {}".format(to_fetch)
          )
  
file_path: ray/rllib/utils/tf_run_builder.py
imports: import logging
  import os
  import time
  from ray.util.debug import log_once
  from ray.rllib.utils.framework import try_import_tf
label_desc: Use image node_id 4 for calling the _TFRunBuilder obj's underlying member method code with example usage: obj.get(to_fetch) and returns: self, unknown, tuple