class_docstr: Callback to save multi_gpu_model
class_docstr_tok: ["Callback","to","save","multi_gpu_model"]
code: def __init__(
      self,
      model,
      filepath,
      monitor="val_loss",
      verbose=0,
      save_best_only=False,
      save_weights_only=False,
      mode="auto",
      save_freq="epoch",
      load_weights_on_restart=False,
      period=1,
  ):
      self.model_to_save = model
      super().__init__(
          filepath=filepath,
          monitor=monitor,
          verbose=verbose,
          save_best_only=save_best_only,
          save_weights_only=save_weights_only,
          mode=mode,
          save_freq=save_freq,
          load_weights_on_restart=load_weights_on_restart,
          period=period,
      )
  
imports: from absl import logging
  import delta.compat
  import numpy
  from tensorflow.keras.callbacks import Callback
  from tensorflow.keras.callbacks import ModelCheckpoint
  from tensorflow.keras import backend
  from sklearn import metrics
  from tensorflow.python.data.ops import iterator_ops
  from tensorflow.python.data.ops import dataset_ops
  from delta.utils.decode import py_ctc
  from delta.utils.decode import tf_ctc
  from delta.utils import metrics
label_desc: Use image node_id 1 to create a new ParallelModelCheckpoint object from inherited base classes: ModelCheckpoint with example: obj = ParallelModelCheckpoint(model, filepath, monitor, verbose, save_best_only, save_weights_only, mode, save_freq, load_weights_on_restart, period)