code: def predict(model, image_files, resize=False, show_image=True, cloud=None):
    
  
    print('Predicting...')
    with warnings.catch_warnings():
      warnings.simplefilter("ignore")
      if cloud is None:
        results = _local.Local.predict(model, image_files, resize, show_image)
      else:
        results = _cloud.Cloud.predict(model, image_files, resize, show_image)
      return results
docs: Predict using an model in a local or GCS directory (offline), or a deployed model (online).
  
    Args:
      model: if cloud is None, a local or GCS directory of a trained model. Otherwise, it specifies
          a deployed model identified by model.version, such as "imagemodel.v1".
      image_files: The paths to the image files to predict labels. Can be local or GCS paths.
      resize: Whether to resize the image to a reasonable size (300x300) before prediction.
      show_image: Whether to show images in the results.
      cloud: if None, predicts with offline model locally. Otherwise, predict with a deployed online
          model.
  
    Returns:
      A pandas DataFrame including the prediction results.