code: def run_training(epochs=1):
      # Create a MirroredStrategy.
      strategy = tf.distribute.MirroredStrategy()
  
      # Open a strategy scope and create/restore the model
      with strategy.scope():
          model = make_or_restore_model()
  
          callbacks = [
              # This callback saves a SavedModel every epoch
              # We include the current epoch in the folder name.
              keras.callbacks.ModelCheckpoint(
                  filepath=checkpoint_dir + "/ckpt-{epoch}.keras",
                  save_freq="epoch",
              )
          ]
          model.fit(
              train_dataset,
              epochs=epochs,
              callbacks=callbacks,
              validation_data=val_dataset,
              verbose=2,
          )
  
imports: import os
  import tensorflow
  import keras
label_desc: Use image node_id 4 for calling a global function with example usage: run_training(epochs) without return types