class_docstr: Args:
      in_channels (int): the channel number of input to CenterNetHead.
      num_classes (int): the number of classes, 80 (COCO dataset) by default.
      head_planes (int): the channel number in all head, 256 by default.
      prior_bias (float): prior bias in heatmap head, -2.19 by default, -4.6 in CenterTrack
      regress_ltrb (bool): whether to regress left/top/right/bottom or
          width/height for a box, True by default.
      size_loss (str): the type of size regression loss, 'L1' by default, can be 'giou'.
      loss_weight (dict): the weight of each loss.
      add_iou (bool): whether to add iou branch, False by default.
class_docstr_tok: ["Args",":","in_channels","(","int",")",":","the","channel","number","of","input","to","CenterNetHead",".","num_classes","(","int",")",":","the","number","of","classes",",","80","(","COCO","dataset",")","by","default",".","head_planes","(","int",")",":","the","channel","number","in","all","head",",","256","by","default",".","prior_bias","(","float",")",":","prior","bias","in","heatmap","head",",","-2.19","by","default",",","-4.6","in","CenterTrack","regress_ltrb","(","bool",")",":","whether","to","regress","left\/top\/right\/bottom","or","width\/height","for","a","box",",","True","by","default",".","size_loss","(","str",")",":","the","type","of","size","regression","loss",",","'L1","'","by","default",",","can","be","'giou","'",".","loss_weight","(","dict",")",":","the","weight","of","each","loss",".","add_iou","(","bool",")",":","whether","to","add","iou","branch",",","False","by","default","."]
code: def from_config(cls, cfg, input_shape):
      if isinstance(input_shape, (list, tuple)):
          input_shape = input_shape[0]
      return {"in_channels": input_shape.channels}
  
file_path: PaddleDetection/ppdet/modeling/heads/centernet_head.py
imports: import math
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle.nn.initializer import Constant, Uniform
  from ppdet.core.workspace import register
  from ppdet.modeling.losses import CTFocalLoss, GIoULoss
label_desc: Use image node_id 2 for calling the CenterNetHead obj's underlying member method code with example usage: obj.from_config(cls, cfg, input_shape) and returns: dict