def __init__(self, name=None, channel_means=(0.0, 0.0, 0.0), channel_stds=(1.0, 1.0, 1.0), bgr_ordering=False):
    'Initializes a CenterNet feature extractor.\n\n    Args:\n      name: str, the name used for the underlying keras model.\n      channel_means: A tuple of floats, denoting the mean of each channel\n        which will be subtracted from it. If None or empty, we use 0s.\n      channel_stds: A tuple of floats, denoting the standard deviation of each\n        channel. Each channel will be divided by its standard deviation value.\n        If None or empty, we use 1s.\n      bgr_ordering: bool, if set will change the channel ordering to be in the\n        [blue, red, green] order.\n    '
    super(CenterNetFeatureExtractor, self).__init__(name=name)
    if ((channel_means is None) or (len(channel_means) == 0)):
        channel_means = [0.0, 0.0, 0.0]
    if ((channel_stds is None) or (len(channel_stds) == 0)):
        channel_stds = [1.0, 1.0, 1.0]
    self._channel_means = channel_means
    self._channel_stds = channel_stds
    self._bgr_ordering = bgr_ordering
Initializes a CenterNet feature extractor.

Args:
  name: str, the name used for the underlying keras model.
  channel_means: A tuple of floats, denoting the mean of each channel
    which will be subtracted from it. If None or empty, we use 0s.
  channel_stds: A tuple of floats, denoting the standard deviation of each
    channel. Each channel will be divided by its standard deviation value.
    If None or empty, we use 1s.
  bgr_ordering: bool, if set will change the channel ordering to be in the
    [blue, red, green] order.
def __init__(self, name=None, channel_means=(0.0, 0.0, 0.0), channel_stds=(1.0, 1.0, 1.0), bgr_ordering=False):
    'Initializes a CenterNet feature extractor.\n\n    Args:\n      name: str, the name used for the underlying keras model.\n      channel_means: A tuple of floats, denoting the mean of each channel\n        which will be subtracted from it. If None or empty, we use 0s.\n      channel_stds: A tuple of floats, denoting the standard deviation of each\n        channel. Each channel will be divided by its standard deviation value.\n        If None or empty, we use 1s.\n      bgr_ordering: bool, if set will change the channel ordering to be in the\n        [blue, red, green] order.\n    '
    super(CenterNetFeatureExtractor, self).__init__(name=name)
    if ((channel_means is None) or (len(channel_means) == 0)):
        channel_means = [0.0, 0.0, 0.0]
    if ((channel_stds is None) or (len(channel_stds) == 0)):
        channel_stds = [1.0, 1.0, 1.0]
    self._channel_means = channel_means
    self._channel_stds = channel_stds
    self._bgr_ordering = bgr_ordering