human
Complete this python program:
def get_preprocessing(name, is_training=False):
assistant
#Here is the Python code you requested.

    """Returns preprocessing_fn(image, height, width, **kwargs).
    Args:
    name: The name of the preprocessing function.
    is_training: `True` if the model is being used for training and `False`
    otherwise.
    Returns:
    preprocessing_fn: A function that preprocessing a single image (pre-batch).
    It has the following signature:
    image = preprocessing_fn(image, output_height, output_width, ...).
    Raises:
    ValueError: If Preprocessing `name` is not recognized.
    """
    preprocessing_fn_map = {'cifarnet': cifarnet_preprocessing, 'inception': inception_preprocessing, 'inception_v1': inception_preprocessing, 'inception_v2': inception_preprocessing, 'inception_v3': inception_preprocessing, 'inception_v4': inception_preprocessing, 'inception_resnet_v2': inception_preprocessing, 'lenet': lenet_preprocessing, 'resnet_v1_50': vgg_preprocessing, 'resnet_v1_101': vgg_preprocessing, 'resnet_v1_152': vgg_preprocessing, 'resnet_v2_50': vgg_preprocessing, 'resnet_v2_101': vgg_preprocessing, 'resnet_v2_152': vgg_preprocessing, 'vgg': vgg_preprocessing, 'vgg_a': vgg_preprocessing, 'vgg_16': vgg_preprocessing, 'vgg_19': vgg_preprocessing}
    if (name not in preprocessing_fn_map):
        raise ValueError(('Preprocessing    name    [%s]    was    not    recognized' % name))
    def preprocessing_fn(image, output_height, output_width, **kwargs):
        return preprocessing_fn_map[name].preprocess_image(image, output_height, output_width, is_training=is_training, **kwargs)
    return preprocessing_fn