def _initialize_target_assigners(self, stride, min_box_overlap_iou):
    'Initializes the target assigners and puts them in a dictionary.\n\n    Args:\n      stride: An integer indicating the stride of the image.\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\n    Returns:\n      A dictionary of initialized target assigners for each task.\n    '
    target_assigners = {}
    target_assigners[OBJECT_CENTER] = cn_assigner.CenterNetCenterHeatmapTargetAssigner(stride, min_box_overlap_iou)
    if (self._od_params is not None):
        target_assigners[DETECTION_TASK] = cn_assigner.CenterNetBoxTargetAssigner(stride)
    if (self._kp_params_dict is not None):
        for (task_name, kp_params) in self._kp_params_dict.items():
            target_assigners[task_name] = cn_assigner.CenterNetKeypointTargetAssigner(stride=stride, class_id=kp_params.class_id, keypoint_indices=kp_params.keypoint_indices, keypoint_std_dev=kp_params.keypoint_std_dev, peak_radius=kp_params.offset_peak_radius, per_keypoint_offset=kp_params.per_keypoint_offset)
    if (self._mask_params is not None):
        target_assigners[SEGMENTATION_TASK] = cn_assigner.CenterNetMaskTargetAssigner(stride)
    if (self._densepose_params is not None):
        dp_stride = (1 if self._densepose_params.upsample_to_input_res else stride)
        target_assigners[DENSEPOSE_TASK] = cn_assigner.CenterNetDensePoseTargetAssigner(dp_stride)
    if (self._track_params is not None):
        target_assigners[TRACK_TASK] = cn_assigner.CenterNetTrackTargetAssigner(stride, self._track_params.num_track_ids)
    if (self._temporal_offset_params is not None):
        target_assigners[TEMPORALOFFSET_TASK] = cn_assigner.CenterNetTemporalOffsetTargetAssigner(stride)
    return target_assigners
Initializes the target assigners and puts them in a dictionary.

Args:
  stride: An integer indicating the stride of the image.
  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.

Returns:
  A dictionary of initialized target assigners for each task.
def _initialize_target_assigners(self, stride, min_box_overlap_iou):
    'Initializes the target assigners and puts them in a dictionary.\n\n    Args:\n      stride: An integer indicating the stride of the image.\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\n    Returns:\n      A dictionary of initialized target assigners for each task.\n    '
    target_assigners = {}
    target_assigners[OBJECT_CENTER] = cn_assigner.CenterNetCenterHeatmapTargetAssigner(stride, min_box_overlap_iou)
    if (self._od_params is not None):
        target_assigners[DETECTION_TASK] = cn_assigner.CenterNetBoxTargetAssigner(stride)
    if (self._kp_params_dict is not None):
        for (task_name, kp_params) in self._kp_params_dict.items():
            target_assigners[task_name] = cn_assigner.CenterNetKeypointTargetAssigner(stride=stride, class_id=kp_params.class_id, keypoint_indices=kp_params.keypoint_indices, keypoint_std_dev=kp_params.keypoint_std_dev, peak_radius=kp_params.offset_peak_radius, per_keypoint_offset=kp_params.per_keypoint_offset)
    if (self._mask_params is not None):
        target_assigners[SEGMENTATION_TASK] = cn_assigner.CenterNetMaskTargetAssigner(stride)
    if (self._densepose_params is not None):
        dp_stride = (1 if self._densepose_params.upsample_to_input_res else stride)
        target_assigners[DENSEPOSE_TASK] = cn_assigner.CenterNetDensePoseTargetAssigner(dp_stride)
    if (self._track_params is not None):
        target_assigners[TRACK_TASK] = cn_assigner.CenterNetTrackTargetAssigner(stride, self._track_params.num_track_ids)
    if (self._temporal_offset_params is not None):
        target_assigners[TEMPORALOFFSET_TASK] = cn_assigner.CenterNetTemporalOffsetTargetAssigner(stride)
    return target_assigners