class_docstr: HrHRNet postprocess contain:
      1) get topk keypoints in the output heatmap
      2) sample the tagmap's value corresponding to each of the topk coordinate
      3) match different joints to combine to some people with Hungary algorithm
      4) adjust the coordinate by +-0.25 to decrease error std
      5) salvage missing joints by check positivity of heatmap - tagdiff_norm
  Args:
      max_num_people (int): max number of people support in postprocess
      heat_thresh (float): value of topk below this threshhold will be ignored
      tag_thresh (float): coord's value sampled in tagmap below this threshold belong to same people for init
  
      inputs(list[heatmap]): the output list of model, [heatmap, heatmap_maxpool, tagmap], heatmap_maxpool used to get topk
      original_height, original_width (float): the original image size
class_docstr_tok: ["HrHRNet","postprocess","contain",":","1",")","get","topk","keypoints","in","the","output","heatmap","2",")","sample","the","tagmap","'s","value","corresponding","to","each","of","the","topk","coordinate","3",")","match","different","joints","to","combine","to","some","people","with","Hungary","algorithm","4",")","adjust","the","coordinate","by","+-0.25","to","decrease","error","std","5",")","salvage","missing","joints","by","check","positivity","of","heatmap","-","tagdiff_norm","Args",":","max_num_people","(","int",")",":","max","number","of","people","support","in","postprocess","heat_thresh","(","float",")",":","value","of","topk","below","this","threshhold","will","be","ignored","tag_thresh","(","float",")",":","coord","'s","value","sampled","in","tagmap","below","this","threshold","belong","to","same","people","for","init","inputs","(","list","[","heatmap","]",")",":","the","output","list","of","model",",","[","heatmap",",","heatmap_maxpool",",","tagmap","]",",","heatmap_maxpool","used","to","get","topk","original_height",",","original_width","(","float",")",":","the","original","image","size"]
code: def lerp(self, j, y, x, heatmap):
      H, W = heatmap.shape[-2:]
      left = np.clip(x - 1, 0, W - 1)
      right = np.clip(x + 1, 0, W - 1)
      up = np.clip(y - 1, 0, H - 1)
      down = np.clip(y + 1, 0, H - 1)
      offset_y = np.where(
          heatmap[j, down, x] > heatmap[j, up, x], 0.25, -0.25
      )
      offset_x = np.where(
          heatmap[j, y, right] > heatmap[j, y, left], 0.25, -0.25
      )
      return offset_y + 0.5, offset_x + 0.5
  
imports: from scipy.optimize import linear_sum_assignment
  from collections import abc, defaultdict
  import cv2
  import numpy
  import math
  import paddle
  import paddle.nn
  from keypoint_preprocess import get_affine_mat_kernel, get_affine_transform
label_desc: Use image node_id 2 for calling the HrHRNetPostProcess obj's underlying member method code with example usage: obj.lerp(j, y, x, heatmap) and returns: unknown,  unknown