code: def forward_for_single_feature_map_tvm(
      self,
      locations,
      logits_pred,
      bbox_reg_pred,
      kpt_reg_pred,
      kpt_vis_pred,
      ctrness_pred,
      image_sizes,
      top_feat=None,
  ):
      N, C, H, W = logits_pred.shape
  
      # put in the same format as locations
      logits_pred = logits_pred.view(N, C, H, W).permute(0, 2, 3, 1)
      logits_pred = logits_pred.reshape(N, -1, C).sigmoid()
      kpt_regression = kpt_reg_pred.view(
          N, self.num_kpts * 2, H, W
      ).permute(0, 2, 3, 1)
      kpt_regression = kpt_regression.reshape(N, -1, self.num_kpts, 2)
      ctrness_pred = ctrness_pred.view(N, 1, H, W).permute(0, 2, 3, 1)
      ctrness_pred = ctrness_pred.reshape(N, -1).sigmoid()
      if self.enable_bbox_branch:
          box_regression = bbox_reg_pred.view(N, 4, H, W).permute(
              0, 2, 3, 1
          )
          box_regression = box_regression.reshape(N, -1, 4)
      if self.enable_kpt_vis_branch:
          kpt_vis_pred = (
              kpt_vis_pred.permute(0, 2, 3, 1)
              .reshape(N, -1, self.num_kpts)
              .sigmoid()
          )
      if top_feat is not None:
          top_feat = top_feat.view(N, -1, H, W).permute(0, 2, 3, 1)
          top_feat = top_feat.reshape(N, H * W, -1)
  
      # if self.thresh_with_ctr is True, we multiply the classification
      # scores with centerness scores before applying the threshold.
      if self.thresh_with_ctr:
          logits_pred = logits_pred * ctrness_pred[:, :, None]
      # candidate_inds = logits_pred > self.pre_nms_thresh
      # pre_nms_top_n = candidate_inds.view(N, -1).sum(1)
      # pre_nms_top_n = pre_nms_top_n.clamp(max=self.pre_nms_topk)
  
      if not self.thresh_with_ctr:
          logits_pred = logits_pred * ctrness_pred[:, :, None]
  
      results = []
      for i in range(N):
          assert (
              logits_pred.shape[-1] == 1
          ), "single detection class only"
          per_box_cls = logits_pred[i]
          mask = per_box_cls <= self.pre_nms_thresh
          per_box_cls = per_box_cls.masked_fill(mask, 0)
          # per_box_cls = torch.where(mask, per_box_cls, per_box_cls * 0)
          per_class = per_box_cls * 0
          per_class = per_class.masked_fill(mask, -1)
          # per_candidate_inds = candidate_inds[i]
          # per_box_cls = per_box_cls[per_candidate_inds]
  
          # per_candidate_nonzeros = per_candidate_inds.nonzero()
          # per_box_loc = per_candidate_nonzeros[:, 0]
          # per_class = per_candidate_nonzeros[:, 1]
  
          per_kpt_regression = kpt_regression[i]
          per_kpt_regression = per_kpt_regression.masked_fill(
              mask.unsqueeze(-1), 0
          )
          # per_kpt_regression = per_kpt_regression[per_box_loc]
          # per_locations = locations[per_box_loc]
          per_locations = locations.clone()
          per_locations = per_locations.masked_fill(mask, 0)
  
          if self.enable_bbox_branch:
              per_box_regression = box_regression[i]
              per_box_regression = per_box_regression.masked_fill(
                  mask, 0
              )
              # per_box_regression = per_box_regression[per_box_loc]
          if self.enable_kpt_vis_branch:
              per_kpt_vis = kpt_vis_pred[i]
              per_kpt_vis = per_kpt_vis.masked_fill(mask, 0)
              # per_kpt_vis = per_kpt_vis[per_box_loc]
          if top_feat is not None:
              per_top_feat = top_feat[i]
              per_top_feat = per_top_feat.masked_fill(mask, 0)
              # per_top_feat = per_top_feat[per_box_loc]
  
          # per_pre_nms_top_n = pre_nms_top_n[i]
  
          # It will only happen when there are more than 1000 person candidate in the image
          # if per_candidate_inds.sum().item() > per_pre_nms_top_n.item():
          #     per_box_cls, top_k_indices = per_box_cls.topk(per_pre_nms_top_n, sorted=False)
          #     per_class = per_class[top_k_indices]
  
          #     per_kpt_regression = per_kpt_regression[top_k_indices]
          #     per_locations = per_locations[top_k_indices]
          #     if self.enable_bbox_branch:
          #         per_box_regression = per_box_regression[top_k_indices]
          #     if self.enable_kpt_vis_branch:
          #         per_kpt_vis = per_kpt_vis[top_k_indices]
          #     if top_feat is not None:
          #         per_top_feat = per_top_feat[top_k_indices]
  
          if self.enable_kpt_vis_branch:
              keypoints = torch.stack(
                  [
                      per_locations[:, 0:1]
                      + per_kpt_regression[:, :, 0],
                      per_locations[:, 1:]
                      + per_kpt_regression[:, :, 1],
                      per_kpt_vis,
                  ],
                  dim=2,
              )
          else:
              keypoints = torch.stack(
                  [
                      per_locations[:, 0:1]
                      + per_kpt_regression[:, :, 0],
                      per_locations[:, 1:]
                      + per_kpt_regression[:, :, 1],
                      torch.sqrt(per_box_cls)[:, None].expand(
                          -1, self.num_kpts
                      ),
                  ],
                  dim=2,
              )
  
          if self.enable_bbox_branch:
              detections = torch.stack(
                  [
                      per_locations[:, 0] - per_box_regression[:, 0],
                      per_locations[:, 1] - per_box_regression[:, 1],
                      per_locations[:, 0] + per_box_regression[:, 2],
                      per_locations[:, 1] + per_box_regression[:, 3],
                  ],
                  dim=1,
              )
          else:
              min_xy, _ = keypoints.min(dim=1)
              max_xy, _ = keypoints.max(dim=1)
              detections = torch.cat(
                  (min_xy[:, 0:2], max_xy[:, 0:2]), dim=1
              )
              detections = torch.stack(
                  (
                      detections[:, 0].clamp(
                          min=0.0, max=float(image_sizes[i][1])
                      ),
                      detections[:, 1].clamp(
                          min=0.0, max=float(image_sizes[i][0])
                      ),
                      detections[:, 2].clamp(
                          min=0.0, max=float(image_sizes[i][1])
                      ),
                      detections[:, 3].clamp(
                          min=0.0, max=float(image_sizes[i][0])
                      ),
                  ),
                  dim=1,
              )
  
          boxlist = Instances(image_sizes[i])
          boxlist.pred_boxes = Boxes(detections)
          boxlist.pred_keypoints = keypoints
          boxlist.scores = torch.sqrt(per_box_cls.squeeze(-1))
          boxlist.pred_classes = per_class.squeeze(-1)
          boxlist.locations = per_locations
          if top_feat is not None:
              boxlist.top_feat = per_top_feat
          results.append(boxlist)
          # print(boxlist.pred_boxes.tensor.shape, boxlist.pred_keypoints.shape, boxlist.scores.shape, boxlist.pred_classes.shape, boxlist.locations.shape)
  
      return results
  
imports: import logging
  import os
  from typing import List
  import numpy
  import torch
  from torch import nn
  import torch.nn.functional
  from torchvision.ops import nms
  from ...data.structures import Instances, Boxes
  from ...utils.comm import get_world_size
  from ...nn.focal_loss import sigmoid_focal_loss_jit
  from ...nn.smooth_l1_loss import smooth_l1_loss
  from ...utils.comm import reduce_sum
  from ...nn.nms import ml_nms, oks_nms, close_kpt_nms
  from ...nn.iou_loss import IOULoss
  from ...nn.keypoint_loss import WeightedMSELoss, HMFocalLoss
label_desc: Use image node_id 9 for calling the DirectPoseOutputs obj's underlying member method code with example usage: obj.forward_for_single_feature_map_tvm(locations, logits_pred, bbox_reg_pred, kpt_reg_pred, kpt_vis_pred, ctrness_pred, image_sizes, top_feat) and returns: results