def __new__(cls, class_id, classification_loss, localization_loss, part_loss_weight=1.0, coordinate_loss_weight=1.0, num_parts=24, task_loss_weight=1.0, upsample_to_input_res=True, upsample_method='bilinear', heatmap_bias_init=(- 2.19)):
    'Constructor with default values for DensePoseParams.\n\n    Args:\n      class_id: the ID of the class that contains the DensePose groundtruth.\n        This should typically correspond to the "person" class. Note that the ID\n        is 0-based, meaning that class 0 corresponds to the first non-background\n        object class.\n      classification_loss: an object_detection.core.losses.Loss object to\n        compute the loss for the body part predictions in CenterNet.\n      localization_loss: an object_detection.core.losses.Loss object to compute\n        the loss for the surface coordinate regression in CenterNet.\n      part_loss_weight: The loss weight to apply to part prediction.\n      coordinate_loss_weight: The loss weight to apply to surface coordinate\n        prediction.\n      num_parts: The number of DensePose parts to predict.\n      task_loss_weight: float, the loss weight for the DensePose task.\n      upsample_to_input_res: Whether to upsample the DensePose feature maps to\n        the input resolution before applying loss. Note that the prediction\n        outputs are still at the standard CenterNet output stride.\n      upsample_method: Method for upsampling DensePose feature maps. Options are\n        either \'bilinear\' or \'nearest\'). This takes no effect when\n        `upsample_to_input_res` is False.\n      heatmap_bias_init: float, the initial value of bias in the convolutional\n        kernel of the part prediction head. If set to None, the\n        bias is initialized with zeros.\n\n    Returns:\n      An initialized DensePoseParams namedtuple.\n    '
    return super(DensePoseParams, cls).__new__(cls, class_id, classification_loss, localization_loss, part_loss_weight, coordinate_loss_weight, num_parts, task_loss_weight, upsample_to_input_res, upsample_method, heatmap_bias_init)
Constructor with default values for DensePoseParams.

Args:
  class_id: the ID of the class that contains the DensePose groundtruth.
    This should typically correspond to the "person" class. Note that the ID
    is 0-based, meaning that class 0 corresponds to the first non-background
    object class.
  classification_loss: an object_detection.core.losses.Loss object to
    compute the loss for the body part predictions in CenterNet.
  localization_loss: an object_detection.core.losses.Loss object to compute
    the loss for the surface coordinate regression in CenterNet.
  part_loss_weight: The loss weight to apply to part prediction.
  coordinate_loss_weight: The loss weight to apply to surface coordinate
    prediction.
  num_parts: The number of DensePose parts to predict.
  task_loss_weight: float, the loss weight for the DensePose task.
  upsample_to_input_res: Whether to upsample the DensePose feature maps to
    the input resolution before applying loss. Note that the prediction
    outputs are still at the standard CenterNet output stride.
  upsample_method: Method for upsampling DensePose feature maps. Options are
    either 'bilinear' or 'nearest'). This takes no effect when
    `upsample_to_input_res` is False.
  heatmap_bias_init: float, the initial value of bias in the convolutional
    kernel of the part prediction head. If set to None, the
    bias is initialized with zeros.

Returns:
  An initialized DensePoseParams namedtuple.
def __new__(cls, class_id, classification_loss, localization_loss, part_loss_weight=1.0, coordinate_loss_weight=1.0, num_parts=24, task_loss_weight=1.0, upsample_to_input_res=True, upsample_method='bilinear', heatmap_bias_init=(- 2.19)):
    'Constructor with default values for DensePoseParams.\n\n    Args:\n      class_id: the ID of the class that contains the DensePose groundtruth.\n        This should typically correspond to the "person" class. Note that the ID\n        is 0-based, meaning that class 0 corresponds to the first non-background\n        object class.\n      classification_loss: an object_detection.core.losses.Loss object to\n        compute the loss for the body part predictions in CenterNet.\n      localization_loss: an object_detection.core.losses.Loss object to compute\n        the loss for the surface coordinate regression in CenterNet.\n      part_loss_weight: The loss weight to apply to part prediction.\n      coordinate_loss_weight: The loss weight to apply to surface coordinate\n        prediction.\n      num_parts: The number of DensePose parts to predict.\n      task_loss_weight: float, the loss weight for the DensePose task.\n      upsample_to_input_res: Whether to upsample the DensePose feature maps to\n        the input resolution before applying loss. Note that the prediction\n        outputs are still at the standard CenterNet output stride.\n      upsample_method: Method for upsampling DensePose feature maps. Options are\n        either \'bilinear\' or \'nearest\'). This takes no effect when\n        `upsample_to_input_res` is False.\n      heatmap_bias_init: float, the initial value of bias in the convolutional\n        kernel of the part prediction head. If set to None, the\n        bias is initialized with zeros.\n\n    Returns:\n      An initialized DensePoseParams namedtuple.\n    '
    return super(DensePoseParams, cls).__new__(cls, class_id, classification_loss, localization_loss, part_loss_weight, coordinate_loss_weight, num_parts, task_loss_weight, upsample_to_input_res, upsample_method, heatmap_bias_init)