def __init__(self, apply_nms: bool=True, pre_nms_top_k: int=5000, pre_nms_score_threshold: float=0.05, nms_iou_threshold: float=0.5, max_num_detections: int=100, nms_version: str='v1', use_cpu_nms: bool=False, soft_nms_sigma: Optional[float]=None, **kwargs):
    'Initializes a multi-level detection generator.\n\n    Args:\n      apply_nms: A `bool` of whether or not apply non maximum suppression. If\n        False, the decoded boxes and their scores are returned.\n      pre_nms_top_k: An `int` of the number of top scores proposals to be kept\n        before applying NMS.\n      pre_nms_score_threshold: A `float` of the score threshold to apply before\n        applying NMS. Proposals whose scores are below this threshold are thrown\n        away.\n      nms_iou_threshold: A `float` in [0, 1], the NMS IoU threshold.\n      max_num_detections: An `int` of the final number of total detections to\n        generate.\n      nms_version: A string of `batched`, `v1` or `v2` specifies NMS version\n      use_cpu_nms: A `bool` of whether or not enforce NMS to run on CPU.\n      soft_nms_sigma: A `float` representing the sigma parameter for Soft NMS.\n        When soft_nms_sigma=0.0, we fall back to standard NMS.\n      **kwargs: Additional keyword arguments passed to Layer.\n    '
    self._config_dict = {'apply_nms': apply_nms, 'pre_nms_top_k': pre_nms_top_k, 'pre_nms_score_threshold': pre_nms_score_threshold, 'nms_iou_threshold': nms_iou_threshold, 'max_num_detections': max_num_detections, 'nms_version': nms_version, 'use_cpu_nms': use_cpu_nms, 'soft_nms_sigma': soft_nms_sigma}
    super(MultilevelDetectionGenerator, self).__init__(**kwargs)
Initializes a multi-level detection generator.

Args:
  apply_nms: A `bool` of whether or not apply non maximum suppression. If
    False, the decoded boxes and their scores are returned.
  pre_nms_top_k: An `int` of the number of top scores proposals to be kept
    before applying NMS.
  pre_nms_score_threshold: A `float` of the score threshold to apply before
    applying NMS. Proposals whose scores are below this threshold are thrown
    away.
  nms_iou_threshold: A `float` in [0, 1], the NMS IoU threshold.
  max_num_detections: An `int` of the final number of total detections to
    generate.
  nms_version: A string of `batched`, `v1` or `v2` specifies NMS version
  use_cpu_nms: A `bool` of whether or not enforce NMS to run on CPU.
  soft_nms_sigma: A `float` representing the sigma parameter for Soft NMS.
    When soft_nms_sigma=0.0, we fall back to standard NMS.
  **kwargs: Additional keyword arguments passed to Layer.
def __init__(self, apply_nms: bool=True, pre_nms_top_k: int=5000, pre_nms_score_threshold: float=0.05, nms_iou_threshold: float=0.5, max_num_detections: int=100, nms_version: str='v1', use_cpu_nms: bool=False, soft_nms_sigma: Optional[float]=None, **kwargs):
    'Initializes a multi-level detection generator.\n\n    Args:\n      apply_nms: A `bool` of whether or not apply non maximum suppression. If\n        False, the decoded boxes and their scores are returned.\n      pre_nms_top_k: An `int` of the number of top scores proposals to be kept\n        before applying NMS.\n      pre_nms_score_threshold: A `float` of the score threshold to apply before\n        applying NMS. Proposals whose scores are below this threshold are thrown\n        away.\n      nms_iou_threshold: A `float` in [0, 1], the NMS IoU threshold.\n      max_num_detections: An `int` of the final number of total detections to\n        generate.\n      nms_version: A string of `batched`, `v1` or `v2` specifies NMS version\n      use_cpu_nms: A `bool` of whether or not enforce NMS to run on CPU.\n      soft_nms_sigma: A `float` representing the sigma parameter for Soft NMS.\n        When soft_nms_sigma=0.0, we fall back to standard NMS.\n      **kwargs: Additional keyword arguments passed to Layer.\n    '
    self._config_dict = {'apply_nms': apply_nms, 'pre_nms_top_k': pre_nms_top_k, 'pre_nms_score_threshold': pre_nms_score_threshold, 'nms_iou_threshold': nms_iou_threshold, 'max_num_detections': max_num_detections, 'nms_version': nms_version, 'use_cpu_nms': use_cpu_nms, 'soft_nms_sigma': soft_nms_sigma}
    super(MultilevelDetectionGenerator, self).__init__(**kwargs)