code: def _forward_obj_encoding(self, sample_list, fwd_results):
      # object appearance feature: Faster R-CNN fc7
      obj_fc6 = sample_list.image_feature_0
      obj_fc7 = self.obj_faster_rcnn_fc7(obj_fc6)
      obj_fc7 = F.normalize(obj_fc7, dim=-1)
  
      obj_feat = obj_fc7
      obj_bbox = sample_list.obj_bbox_coordinates
      obj_mmt_in = self.obj_feat_layer_norm(
          self.linear_obj_feat_to_mmt_in(obj_feat)
      ) + self.obj_bbox_layer_norm(
          self.linear_obj_bbox_to_mmt_in(obj_bbox)
      )
      obj_mmt_in = self.obj_drop(obj_mmt_in)
      fwd_results["obj_mmt_in"] = obj_mmt_in
  
      # binary mask of valid object vs padding
      obj_nums = sample_list.image_info_0.max_features
      fwd_results["obj_mask"] = _get_mask(obj_nums, obj_mmt_in.size(1))
  
imports: import functools
  import logging
  import math
  import torch
  import torch.nn.functional
  from mmf.common.registry import registry
  from mmf.models.base_model import BaseModel
  from mmf.modules.layers import ClassifierLayer
  from mmf.utils.build import build_image_encoder
  from omegaconf import OmegaConf
  from torch import nn
label_desc: Use image node_id 12 for calling the M4C obj's underlying member method code with example usage: obj._forward_obj_encoding(sample_list, fwd_results) without return types