def __new__(cls, task_name, class_id, keypoint_indices, classification_loss, localization_loss, keypoint_labels=None, keypoint_std_dev=None, keypoint_heatmap_loss_weight=1.0, keypoint_offset_loss_weight=1.0, keypoint_regression_loss_weight=1.0, keypoint_candidate_score_threshold=0.1, heatmap_bias_init=(- 2.19), num_candidates_per_keypoint=100, task_loss_weight=1.0, peak_max_pool_kernel_size=3, unmatched_keypoint_score=0.1, box_scale=1.2, candidate_search_scale=0.3, candidate_ranking_mode='min_distance', offset_peak_radius=0, per_keypoint_offset=False):
    'Constructor with default values for KeypointEstimationParams.\n\n    Args:\n      task_name: string, the name of the task this namedtuple corresponds to.\n        Note that it should be an unique identifier of the task.\n      class_id: int, the ID of the class that contains the target keypoints to\n        considered in this task. For example, if the task is human pose\n        estimation, the class id should correspond to the "human" class. Note\n        that the ID is 0-based, meaning that class 0 corresponds to the first\n        non-background object class.\n      keypoint_indices: A list of integers representing the indicies of the\n        keypoints to be considered in this task. This is used to retrieve the\n        subset of the keypoints from gt_keypoints that should be considered in\n        this task.\n      classification_loss: an object_detection.core.losses.Loss object to\n        compute the loss for the class predictions in CenterNet.\n      localization_loss: an object_detection.core.losses.Loss object to compute\n        the loss for the center offset and height/width predictions in\n        CenterNet.\n      keypoint_labels: A list of strings representing the label text of each\n        keypoint, e.g. "nose", \'left_shoulder". Note that the length of this\n        list should be equal to keypoint_indices.\n      keypoint_std_dev: A list of float represent the standard deviation of the\n        Gaussian kernel used to generate the keypoint heatmap. It is to provide\n        the flexibility of using different sizes of Gaussian kernel for each\n        keypoint class.\n      keypoint_heatmap_loss_weight: float, The weight for the keypoint heatmap.\n      keypoint_offset_loss_weight: float, The weight for the keypoint offsets\n        loss.\n      keypoint_regression_loss_weight: float, The weight for keypoint regression\n        loss. Note that the loss is dependent on the input image size, since we\n        penalize the raw height and width. This constant may need to be adjusted\n        depending on the input size.\n      keypoint_candidate_score_threshold: float, The heatmap score threshold for\n        a keypoint to become a valid candidate.\n      heatmap_bias_init: float, the initial value of bias in the convolutional\n        kernel of the class prediction head. If set to None, the bias is\n        initialized with zeros.\n      num_candidates_per_keypoint: The maximum number of candidates to retrieve\n        for each keypoint.\n      task_loss_weight: float, the weight of the keypoint estimation loss.\n      peak_max_pool_kernel_size: Max pool kernel size to use to pull off peak\n        score locations in a neighborhood (independently for each keypoint\n        types).\n      unmatched_keypoint_score: The default score to use for regressed keypoints\n        that are not successfully snapped to a nearby candidate.\n      box_scale: The multiplier to expand the bounding boxes (either the\n        provided boxes or those which tightly cover the regressed keypoints).\n      candidate_search_scale: The scale parameter that multiplies the largest\n        dimension of a bounding box. The resulting distance becomes a search\n        radius for candidates in the vicinity of each regressed keypoint.\n      candidate_ranking_mode: One of [\'min_distance\', \'score_distance_ratio\']\n        indicating how to select the keypoint candidate.\n      offset_peak_radius: The radius (in the unit of output pixel) around\n        groundtruth heatmap peak to assign the offset targets. If set 0, then\n        the offset target will only be assigned to the heatmap peak (same\n        behavior as the original paper).\n      per_keypoint_offset: A bool indicates whether to assign offsets for each\n        keypoint channel separately. If set False, the output offset target has\n        the shape [batch_size, out_height, out_width, 2] (same behavior as the\n        original paper). If set True, the output offset target has the shape\n        [batch_size, out_height, out_width, 2 * num_keypoints] (recommended when\n        the offset_peak_radius is not zero).\n\n    Returns:\n      An initialized KeypointEstimationParams namedtuple.\n    '
    return super(KeypointEstimationParams, cls).__new__(cls, task_name, class_id, keypoint_indices, classification_loss, localization_loss, keypoint_labels, keypoint_std_dev, keypoint_heatmap_loss_weight, keypoint_offset_loss_weight, keypoint_regression_loss_weight, keypoint_candidate_score_threshold, heatmap_bias_init, num_candidates_per_keypoint, task_loss_weight, peak_max_pool_kernel_size, unmatched_keypoint_score, box_scale, candidate_search_scale, candidate_ranking_mode, offset_peak_radius, per_keypoint_offset)
Constructor with default values for KeypointEstimationParams.

Args:
  task_name: string, the name of the task this namedtuple corresponds to.
    Note that it should be an unique identifier of the task.
  class_id: int, the ID of the class that contains the target keypoints to
    considered in this task. For example, if the task is human pose
    estimation, the class id should correspond to the "human" class. Note
    that the ID is 0-based, meaning that class 0 corresponds to the first
    non-background object class.
  keypoint_indices: A list of integers representing the indicies of the
    keypoints to be considered in this task. This is used to retrieve the
    subset of the keypoints from gt_keypoints that should be considered in
    this task.
  classification_loss: an object_detection.core.losses.Loss object to
    compute the loss for the class predictions in CenterNet.
  localization_loss: an object_detection.core.losses.Loss object to compute
    the loss for the center offset and height/width predictions in
    CenterNet.
  keypoint_labels: A list of strings representing the label text of each
    keypoint, e.g. "nose", 'left_shoulder". Note that the length of this
    list should be equal to keypoint_indices.
  keypoint_std_dev: A list of float represent the standard deviation of the
    Gaussian kernel used to generate the keypoint heatmap. It is to provide
    the flexibility of using different sizes of Gaussian kernel for each
    keypoint class.
  keypoint_heatmap_loss_weight: float, The weight for the keypoint heatmap.
  keypoint_offset_loss_weight: float, The weight for the keypoint offsets
    loss.
  keypoint_regression_loss_weight: float, The weight for keypoint regression
    loss. Note that the loss is dependent on the input image size, since we
    penalize the raw height and width. This constant may need to be adjusted
    depending on the input size.
  keypoint_candidate_score_threshold: float, The heatmap score threshold for
    a keypoint to become a valid candidate.
  heatmap_bias_init: float, the initial value of bias in the convolutional
    kernel of the class prediction head. If set to None, the bias is
    initialized with zeros.
  num_candidates_per_keypoint: The maximum number of candidates to retrieve
    for each keypoint.
  task_loss_weight: float, the weight of the keypoint estimation loss.
  peak_max_pool_kernel_size: Max pool kernel size to use to pull off peak
    score locations in a neighborhood (independently for each keypoint
    types).
  unmatched_keypoint_score: The default score to use for regressed keypoints
    that are not successfully snapped to a nearby candidate.
  box_scale: The multiplier to expand the bounding boxes (either the
    provided boxes or those which tightly cover the regressed keypoints).
  candidate_search_scale: The scale parameter that multiplies the largest
    dimension of a bounding box. The resulting distance becomes a search
    radius for candidates in the vicinity of each regressed keypoint.
  candidate_ranking_mode: One of ['min_distance', 'score_distance_ratio']
    indicating how to select the keypoint candidate.
  offset_peak_radius: The radius (in the unit of output pixel) around
    groundtruth heatmap peak to assign the offset targets. If set 0, then
    the offset target will only be assigned to the heatmap peak (same
    behavior as the original paper).
  per_keypoint_offset: A bool indicates whether to assign offsets for each
    keypoint channel separately. If set False, the output offset target has
    the shape [batch_size, out_height, out_width, 2] (same behavior as the
    original paper). If set True, the output offset target has the shape
    [batch_size, out_height, out_width, 2 * num_keypoints] (recommended when
    the offset_peak_radius is not zero).

Returns:
  An initialized KeypointEstimationParams namedtuple.
def __new__(cls, task_name, class_id, keypoint_indices, classification_loss, localization_loss, keypoint_labels=None, keypoint_std_dev=None, keypoint_heatmap_loss_weight=1.0, keypoint_offset_loss_weight=1.0, keypoint_regression_loss_weight=1.0, keypoint_candidate_score_threshold=0.1, heatmap_bias_init=(- 2.19), num_candidates_per_keypoint=100, task_loss_weight=1.0, peak_max_pool_kernel_size=3, unmatched_keypoint_score=0.1, box_scale=1.2, candidate_search_scale=0.3, candidate_ranking_mode='min_distance', offset_peak_radius=0, per_keypoint_offset=False):
    'Constructor with default values for KeypointEstimationParams.\n\n    Args:\n      task_name: string, the name of the task this namedtuple corresponds to.\n        Note that it should be an unique identifier of the task.\n      class_id: int, the ID of the class that contains the target keypoints to\n        considered in this task. For example, if the task is human pose\n        estimation, the class id should correspond to the "human" class. Note\n        that the ID is 0-based, meaning that class 0 corresponds to the first\n        non-background object class.\n      keypoint_indices: A list of integers representing the indicies of the\n        keypoints to be considered in this task. This is used to retrieve the\n        subset of the keypoints from gt_keypoints that should be considered in\n        this task.\n      classification_loss: an object_detection.core.losses.Loss object to\n        compute the loss for the class predictions in CenterNet.\n      localization_loss: an object_detection.core.losses.Loss object to compute\n        the loss for the center offset and height/width predictions in\n        CenterNet.\n      keypoint_labels: A list of strings representing the label text of each\n        keypoint, e.g. "nose", \'left_shoulder". Note that the length of this\n        list should be equal to keypoint_indices.\n      keypoint_std_dev: A list of float represent the standard deviation of the\n        Gaussian kernel used to generate the keypoint heatmap. It is to provide\n        the flexibility of using different sizes of Gaussian kernel for each\n        keypoint class.\n      keypoint_heatmap_loss_weight: float, The weight for the keypoint heatmap.\n      keypoint_offset_loss_weight: float, The weight for the keypoint offsets\n        loss.\n      keypoint_regression_loss_weight: float, The weight for keypoint regression\n        loss. Note that the loss is dependent on the input image size, since we\n        penalize the raw height and width. This constant may need to be adjusted\n        depending on the input size.\n      keypoint_candidate_score_threshold: float, The heatmap score threshold for\n        a keypoint to become a valid candidate.\n      heatmap_bias_init: float, the initial value of bias in the convolutional\n        kernel of the class prediction head. If set to None, the bias is\n        initialized with zeros.\n      num_candidates_per_keypoint: The maximum number of candidates to retrieve\n        for each keypoint.\n      task_loss_weight: float, the weight of the keypoint estimation loss.\n      peak_max_pool_kernel_size: Max pool kernel size to use to pull off peak\n        score locations in a neighborhood (independently for each keypoint\n        types).\n      unmatched_keypoint_score: The default score to use for regressed keypoints\n        that are not successfully snapped to a nearby candidate.\n      box_scale: The multiplier to expand the bounding boxes (either the\n        provided boxes or those which tightly cover the regressed keypoints).\n      candidate_search_scale: The scale parameter that multiplies the largest\n        dimension of a bounding box. The resulting distance becomes a search\n        radius for candidates in the vicinity of each regressed keypoint.\n      candidate_ranking_mode: One of [\'min_distance\', \'score_distance_ratio\']\n        indicating how to select the keypoint candidate.\n      offset_peak_radius: The radius (in the unit of output pixel) around\n        groundtruth heatmap peak to assign the offset targets. If set 0, then\n        the offset target will only be assigned to the heatmap peak (same\n        behavior as the original paper).\n      per_keypoint_offset: A bool indicates whether to assign offsets for each\n        keypoint channel separately. If set False, the output offset target has\n        the shape [batch_size, out_height, out_width, 2] (same behavior as the\n        original paper). If set True, the output offset target has the shape\n        [batch_size, out_height, out_width, 2 * num_keypoints] (recommended when\n        the offset_peak_radius is not zero).\n\n    Returns:\n      An initialized KeypointEstimationParams namedtuple.\n    '
    return super(KeypointEstimationParams, cls).__new__(cls, task_name, class_id, keypoint_indices, classification_loss, localization_loss, keypoint_labels, keypoint_std_dev, keypoint_heatmap_loss_weight, keypoint_offset_loss_weight, keypoint_regression_loss_weight, keypoint_candidate_score_threshold, heatmap_bias_init, num_candidates_per_keypoint, task_loss_weight, peak_max_pool_kernel_size, unmatched_keypoint_score, box_scale, candidate_search_scale, candidate_ranking_mode, offset_peak_radius, per_keypoint_offset)