class_docstr: This is the DETR module that performs object detection 
class_docstr_tok: ["This","is","the","DETR","module","that","performs","object","detection"]
code: def _set_aux_loss(self, outputs_class, outputs_coord):
      # this is a workaround to make torchscript happy, as torchscript
      # doesn't support dictionary with non-homogeneous values, such
      # as a dict having both a Tensor and a list.
      return [
          {"pred_logits": a, "pred_boxes": b}
          for a, b in zip(outputs_class[:-1], outputs_coord[:-1])
      ]
  
imports: import torch
  import torch.nn.functional
  from torch import nn
  from util import box_ops
  from util.misc import NestedTensor, nested_tensor_from_tensor_list, accuracy, get_world_size, interpolate, is_dist_avail_and_initialized
  from .backbone import build_backbone
  from .matcher import build_matcher
  from .segmentation import DETRsegm, PostProcessPanoptic, PostProcessSegm, dice_loss, sigmoid_focal_loss
  from .transformer import build_transformer
label_desc: Use image node_id 3 for calling the DETR obj's underlying member method code with example usage: obj._set_aux_loss(outputs_class, outputs_coord) and returns: unknown