code: def forward(self, inputs):
      assert self.tensor_mode
      images = self._caffe2_preprocess_image(inputs)
  
      # explicitly return the images sizes to avoid removing "im_info" by ONNX
      # since it's not used in the forward path
      return_tensors = [images.image_sizes]
  
      features = self._wrapped_model.backbone(images.tensor)
      features = [
          features[f] for f in self._wrapped_model.head_in_features
      ]
      for i, feature_i in enumerate(features):
          features[i] = alias(
              feature_i, "feature_{}".format(i), is_backward=True
          )
          return_tensors.append(features[i])
  
      pred_logits, pred_anchor_deltas = self._wrapped_model.head(
          features
      )
      for i, (box_cls_i, box_delta_i) in enumerate(
          zip(pred_logits, pred_anchor_deltas)
      ):
          return_tensors.append(
              alias(box_cls_i, "box_cls_{}".format(i))
          )
          return_tensors.append(
              alias(box_delta_i, "box_delta_{}".format(i))
          )
  
      return tuple(return_tensors)
  
imports: import functools
  import io
  import struct
  import types
  import torch
  from detectron2.modeling import meta_arch
  from detectron2.modeling.box_regression import Box2BoxTransform
  from detectron2.modeling.roi_heads import keypoint_head
  from detectron2.structures import Boxes, ImageList, Instances, RotatedBoxes
  from .c10 import Caffe2Compatible
  from .caffe2_patch import ROIHeadsPatcher, patch_generalized_rcnn
  from .shared import alias, check_set_pb_arg, get_pb_arg_floats, get_pb_arg_valf, get_pb_arg_vali, get_pb_arg_vals, mock_torch_nn_functional_interpolate
label_desc: Use image node_id 2 for calling the Caffe2RetinaNet obj's underlying member method code with example usage: obj.forward(inputs) and returns: tuple