code: def _forward(self):
      batch_size = self.inputs["image"].shape[0]
  
      image_feat = self.backbone(self.inputs)
      image_feat_flatten = image_feat.reshape((batch_size, 2048, 49))
      image_feat_flatten = image_feat_flatten.transpose(perm=(0, 2, 1))
      # and apply a conv layer to learn image token for each 3d joint/vertex position
      features = self.conv_learn_tokens(image_feat_flatten)  # (B, J, C)
  
      if self.training:
          # apply mask vertex/joint modeling
          # meta_masks is a tensor of all the masks, randomly generated in dataloader
          # we pre-define a [MASK] token, which is a floating-value vector with 0.01s
          meta_masks = self.inputs["mjm_mask"].expand((-1, -1, 2048))
          constant_tensor = paddle.ones_like(features) * 0.01
          features = features * meta_masks + constant_tensor * (
              1 - meta_masks
          )
      pred_out = self.trans_encoder(features)
  
      pred_3d_joints = pred_out[:, : self.num_joints, :]
      cam_features = pred_out[:, self.num_joints :, :]
  
      # learn camera parameters
      pred_2d_joints = self.cam_param_fc(cam_features)
      return pred_3d_joints, pred_2d_joints
  
file_path: PaddleDetection/ppdet/modeling/architectures/pose3d_metro.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register, create
  from .meta_arch import BaseArch
  from ..None import layers
label_desc: Use image node_id 3 for calling the METRO_Body obj's underlying member method code with example usage: obj._forward() and returns: pred_3d_joints,  pred_2d_joints