code: def __init__(
      self, object_class_num, attribute_class_num, representation_size
  ):
      super().__init__()
  
      # copy-pasted from
      # https://github.com/ronghanghu/vqa-maskrcnn-benchmark-m4c/blob/0fbccee2dfed10d652bcf014f9f8bfafd8478f51/maskrcnn_benchmark/modeling/roi_heads/box_head/roi_box_predictors.py#L52-L61  # NoQA
      self.cls_embed = nn.Embedding(object_class_num + 1, 256)
      self.attr_linear1 = nn.Linear(representation_size + 256, 512)
      self.attr_linear2 = nn.Linear(512, attribute_class_num)
  
      nn.init.normal_(self.cls_embed.weight, std=0.01)
      nn.init.normal_(self.attr_linear1.weight, std=0.01)
      nn.init.normal_(self.attr_linear2.weight, std=0.01)
      nn.init.constant_(self.attr_linear1.bias, 0)
      nn.init.constant_(self.attr_linear2.bias, 0)
  
imports: from typing import Optional
  import torch
  import torch.nn.functional
  from mmf.models.unit.backbone import build_unit_convnet_backbone
  from mmf.models.unit.matcher import HungarianMatcher
  from mmf.models.unit.misc import NestedTensor
  from mmf.models.unit.transformer import UniTTransformer
  from mmf.utils import box_ops
  from mmf.utils.distributed import get_world_size, is_dist_initialized
  from torch import nn, Tensor
label_desc: Use image node_id 1 to create a new AttributeHead object from inherited base classes: nn with example: obj = AttributeHead(object_class_num, attribute_class_num, representation_size)