code: def _forward(self):
      feats = self.backbone(self.inputs)
      hrnet_outputs = self.final_conv(feats[0])
  
      if self.training:
          return self.loss(hrnet_outputs, self.inputs)
      elif self.deploy:
          outshape = hrnet_outputs.shape
          max_idx = paddle.argmax(
              hrnet_outputs.reshape(
                  (outshape[0], outshape[1], outshape[2] * outshape[3])
              ),
              axis=-1,
          )
          return hrnet_outputs, max_idx
      else:
          if self.flip:
              self.inputs["image"] = self.inputs["image"].flip([3])
              feats = self.backbone(self.inputs)
              output_flipped = self.final_conv(feats[0])
              output_flipped = self.flip_back(
                  output_flipped.numpy(), self.flip_perm
              )
              output_flipped = paddle.to_tensor(output_flipped.copy())
              if self.shift_heatmap:
                  output_flipped[:, :, :, 1:] = output_flipped.clone()[
                      :, :, :, 0:-1
                  ]
              hrnet_outputs = (hrnet_outputs + output_flipped) * 0.5
          imshape = (
              (self.inputs["im_shape"].numpy())[:, ::-1]
              if "im_shape" in self.inputs
              else None
          )
          center = (
              self.inputs["center"].numpy()
              if "center" in self.inputs
              else np.round(imshape / 2.0)
          )
          scale = (
              self.inputs["scale"].numpy()
              if "scale" in self.inputs
              else imshape / 200.0
          )
          outputs = self.post_process(hrnet_outputs, center, scale)
          return outputs
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  import numpy
  import math
  import cv2
  from ppdet.core.workspace import register, create
  from .meta_arch import BaseArch
  from ..keypoint_utils import transform_preds
  from ..None import layers
  from paddle.nn import functional
label_desc: Use image node_id 3 for calling the TopDownHRNet obj's underlying member method code with example usage: obj._forward() and returns: self, hrnet_outputs,  max_idx, outputs