code: def forward(self, x: Tensor) -> Dict[str, Tensor]:
      # extract features from the backbone network
      enc_end_points: Dict = self.encoder.extract_end_points_all(x)
  
      self.augmented_tensor = enc_end_points.pop(
          "augmented_tensor", None
      )
  
      outputs_backbone: Dict = {}
      # project backbone features
      for os, enc_key_name in self.backbone_map.items():
          x_proj = self.backbone_proj_layers[str(os)](
              enc_end_points.pop(enc_key_name)
          )
          outputs_backbone[f"{os}"] = x_proj
  
      if self.fpn_proj_layers:
          # FPN
          last_os = self.backbone_output_strides[-1]
          prev_fm = outputs_backbone[f"{last_os}"]
          prev_fm = self.fpn_proj_layers[f"{last_os}"](prev_fm)
          for os in self.backbone_output_strides[:-1][::-1]:
              curr_fm = outputs_backbone[f"{os}"]
              feat_shape = curr_fm.shape[-2:]
              inner_top_down = F.interpolate(
                  prev_fm, size=feat_shape, mode="nearest"
              )
              prev_fm = self.fpn_proj_layers[f"{os}"](
                  curr_fm + inner_top_down
              )
              outputs_backbone[f"{os}"] = prev_fm
  
      if self.extra_layers:
          prev_os = self.backbone_output_strides[-1]
          for os, extra_layer in self.extra_layers.items():
              x_proj = extra_layer(outputs_backbone[f"{prev_os}"])
              outputs_backbone[f"{os}"] = x_proj
              prev_os = os
      return outputs_backbone
  
imports: import argparse
  from typing import Any, Dict, List, Optional, Tuple, Union
  import torch
  from torch import Tensor, nn
  from torch.nn import functional
  from torchvision.models.detection.anchor_utils import AnchorGenerator
  from torchvision.models.detection.mask_rcnn import MaskRCNN
  from torchvision.ops import MultiScaleRoIAlign
  from cvnets import parameter_list
  from cvnets.layers import ConvLayer2d, Identity
  from cvnets.models import MODEL_REGISTRY
  from cvnets.models.classification.base_image_encoder import BaseImageEncoder
  from cvnets.models.detection import DetectionPredTuple
  from cvnets.models.detection.base_detection import BaseDetection
  from cvnets.models.detection.utils.rcnn_utils import FastRCNNConvFCHead, FastRCNNPredictor, MaskRCNNHeads, MaskRCNNPredictor, RPNHead
  from utils import logger
label_desc: Use image node_id 3 for calling the MaskRCNNEncoder obj's underlying member method code with example usage: obj.forward(x) and returns: outputs_backbone