class_docstr: Manage the double for loop.
docstr: Configure the loop given the settings.
docstr_tok: ["Configure","the","loop","given","the","settings","."]
code: def config(self, steps_per_epoch, starting_epoch, max_epoch):
      """
      Configure the loop given the settings.
      """
      self.starting_epoch = int(starting_epoch)
      self.max_epoch = int(max_epoch)
      self.steps_per_epoch = int(steps_per_epoch)
      # Allow empty epoch (no steps), if we want to run the callbacks only.
      assert self.steps_per_epoch >= 0 and self.max_epoch >= 0
  
      self._epoch_num = starting_epoch - 1
  
imports: import copy
  import time
  import weakref
  import tensorflow
  from ..compat import tfv1
  from ..callbacks import Callback, Callbacks, Monitors, MonitorBase
  from ..callbacks.steps import MaintainStepCounter
  from ..tfutils import get_global_step_value
  from ..tfutils.model_utils import describe_trainable_vars
  from ..tfutils.sesscreate import NewSessionCreator, ReuseSessionCreator
  from ..tfutils.sessinit import JustCurrentSession, SessionInit
  from ..utils import logger
  from ..utils.argtools import call_only_once
  from ..utils.utils import humanize_time_delta
  from .config import DEFAULT_CALLBACKS, DEFAULT_MONITORS, TrainConfig
label_desc: Use image node_id 2 for calling the TrainLoop obj's underlying member method code with example usage: obj.config(steps_per_epoch, starting_epoch, max_epoch) without return types