def __new__(cls, classification_loss, object_center_loss_weight, heatmap_bias_init=(- 2.19), min_box_overlap_iou=0.7, max_box_predictions=100, use_labeled_classes=False):
    'Constructor with default values for ObjectCenterParams.\n\n    Args:\n      classification_loss: an object_detection.core.losses.Loss object to\n        compute the loss for the class predictions in CenterNet.\n      object_center_loss_weight: float, The weight for the object center loss.\n      heatmap_bias_init: float, the initial value of bias in the convolutional\n        kernel of the object center prediction head. If set to None, the bias is\n        initialized with zeros.\n      min_box_overlap_iou: float, the minimum IOU overlap that predicted boxes\n        need have with groundtruth boxes to not be penalized. This is used for\n        computing the class specific center heatmaps.\n      max_box_predictions: int, the maximum number of boxes to predict.\n      use_labeled_classes: boolean, compute the loss only labeled classes.\n\n    Returns:\n      An initialized ObjectCenterParams namedtuple.\n    '
    return super(ObjectCenterParams, cls).__new__(cls, classification_loss, object_center_loss_weight, heatmap_bias_init, min_box_overlap_iou, max_box_predictions, use_labeled_classes)
Constructor with default values for ObjectCenterParams.

Args:
  classification_loss: an object_detection.core.losses.Loss object to
    compute the loss for the class predictions in CenterNet.
  object_center_loss_weight: float, The weight for the object center loss.
  heatmap_bias_init: float, the initial value of bias in the convolutional
    kernel of the object center prediction head. If set to None, the bias is
    initialized with zeros.
  min_box_overlap_iou: float, the minimum IOU overlap that predicted boxes
    need have with groundtruth boxes to not be penalized. This is used for
    computing the class specific center heatmaps.
  max_box_predictions: int, the maximum number of boxes to predict.
  use_labeled_classes: boolean, compute the loss only labeled classes.

Returns:
  An initialized ObjectCenterParams namedtuple.
def __new__(cls, classification_loss, object_center_loss_weight, heatmap_bias_init=(- 2.19), min_box_overlap_iou=0.7, max_box_predictions=100, use_labeled_classes=False):
    'Constructor with default values for ObjectCenterParams.\n\n    Args:\n      classification_loss: an object_detection.core.losses.Loss object to\n        compute the loss for the class predictions in CenterNet.\n      object_center_loss_weight: float, The weight for the object center loss.\n      heatmap_bias_init: float, the initial value of bias in the convolutional\n        kernel of the object center prediction head. If set to None, the bias is\n        initialized with zeros.\n      min_box_overlap_iou: float, the minimum IOU overlap that predicted boxes\n        need have with groundtruth boxes to not be penalized. This is used for\n        computing the class specific center heatmaps.\n      max_box_predictions: int, the maximum number of boxes to predict.\n      use_labeled_classes: boolean, compute the loss only labeled classes.\n\n    Returns:\n      An initialized ObjectCenterParams namedtuple.\n    '
    return super(ObjectCenterParams, cls).__new__(cls, classification_loss, object_center_loss_weight, heatmap_bias_init, min_box_overlap_iou, max_box_predictions, use_labeled_classes)