parameters: (
      train_ops,
      logdir,
      get_hooks_fn=get_sequential_train_hooks(),
      master='',
      is_chief=True,
      scaffold=None,
      hooks=None,
      chief_only_hooks=None,
      save_checkpoint_secs=600,
      save_summaries_steps=100,
      config=None)
return_statement: return training.train(
        train_ops.global_step_inc_op,
        logdir,
        master=master,
        is_chief=is_chief,
        scaffold=scaffold,
        hooks=hooks,
        chief_only_hooks=chief_only_hooks,
        save_checkpoint_secs=save_checkpoint_secs,
        save_summaries_steps=save_summaries_steps,
        config=config)
docstring: A wrapper around `contrib.training.train` that uses GAN hooks.
  
    Args:
      train_ops: A GANTrainOps named tuple.
      logdir: The directory where the graph and checkpoints are saved.
      get_hooks_fn: A function that takes a GANTrainOps tuple and returns a list
        of hooks.
      master: The URL of the master.
      is_chief: Specifies whether or not the training is being run by the primary
        replica during replica training.
      scaffold: An tf.train.Scaffold instance.
      hooks: List of `tf.train.SessionRunHook` callbacks which are run inside the
        training loop.
      chief_only_hooks: List of `tf.train.SessionRunHook` instances which are run
        inside the training loop for the chief trainer only.
      save_checkpoint_secs: The frequency, in seconds, that a checkpoint is saved
        using a default checkpoint saver. If `save_checkpoint_secs` is set to
        `None`, then the default checkpoint saver isn't used.
      save_summaries_steps: The frequency, in number of global steps, that the
        summaries are written to disk using a default summary saver. If
        `save_summaries_steps` is set to `None`, then the default summary saver
        isn't used.
      config: An instance of `tf.ConfigProto`.
  
    Returns:
      Output of the call to `training.train`.
docstring_summary: A wrapper around `contrib.training.train` that uses GAN hooks.
docstring_tokens: A wrapper around contrib . training . train that uses GAN hooks .
function: def gan_train(
      train_ops,
      logdir,
      get_hooks_fn=get_sequential_train_hooks(),
      master='',
      is_chief=True,
      scaffold=None,
      hooks=None,
      chief_only_hooks=None,
      save_checkpoint_secs=600,
      save_summaries_steps=100,
      config=None):
    """A wrapper around `contrib.training.train` that uses GAN hooks.
  
    Args:
      train_ops: A GANTrainOps named tuple.
      logdir: The directory where the graph and checkpoints are saved.
      get_hooks_fn: A function that takes a GANTrainOps tuple and returns a list
        of hooks.
      master: The URL of the master.
      is_chief: Specifies whether or not the training is being run by the primary
        replica during replica training.
      scaffold: An tf.train.Scaffold instance.
      hooks: List of `tf.train.SessionRunHook` callbacks which are run inside the
        training loop.
      chief_only_hooks: List of `tf.train.SessionRunHook` instances which are run
        inside the training loop for the chief trainer only.
      save_checkpoint_secs: The frequency, in seconds, that a checkpoint is saved
        using a default checkpoint saver. If `save_checkpoint_secs` is set to
        `None`, then the default checkpoint saver isn't used.
      save_summaries_steps: The frequency, in number of global steps, that the
        summaries are written to disk using a default summary saver. If
        `save_summaries_steps` is set to `None`, then the default summary saver
        isn't used.
      config: An instance of `tf.ConfigProto`.
  
    Returns:
      Output of the call to `training.train`.
    """
    new_hooks = get_hooks_fn(train_ops)
    if hooks is not None:
      hooks = list(hooks) + list(new_hooks)
    else:
      hooks = new_hooks
    return training.train(
        train_ops.global_step_inc_op,
        logdir,
        master=master,
        is_chief=is_chief,
        scaffold=scaffold,
        hooks=hooks,
        chief_only_hooks=chief_only_hooks,
        save_checkpoint_secs=save_checkpoint_secs,
        save_summaries_steps=save_summaries_steps,
        config=config)
function_tokens: def gan_train ( train_ops , logdir , get_hooks_fn = get_sequential_train_hooks ( ) , master = '' , is_chief = True , scaffold = None , hooks = None , chief_only_hooks = None , save_checkpoint_secs = 600 , save_summaries_steps = 100 , config = None ) : new_hooks = get_hooks_fn ( train_ops ) if hooks is not None : hooks = list ( hooks ) + list ( new_hooks ) else : hooks = new_hooks return training . train ( train_ops . global_step_inc_op , logdir , master = master , is_chief = is_chief , scaffold = scaffold , hooks = hooks , chief_only_hooks = chief_only_hooks , save_checkpoint_secs = save_checkpoint_secs , save_summaries_steps = save_summaries_steps , config = config )