class_docstr: Simplest RoI head, with only two fc layers for classification and
  regression respectively.
class_docstr_tok: ["Simplest","RoI","head",",","with","only","two","fc","layers","for","classification","and","regression","respectively","."]
docstr: get custom_activation from loss_cls.
docstr_tok: ["get","custom_activation","from","loss_cls","."]
code: def custom_activation(self) -> bool:
      """get custom_activation from loss_cls."""
      return getattr(self.loss_cls, "custom_activation", False)
  
imports: from typing import List, Optional, Tuple, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from mmengine.config import ConfigDict
  from mmengine.model import BaseModule
  from mmengine.structures import InstanceData
  from torch import Tensor
  from torch.nn.modules.utils import _pair
  from mmdet.models.layers import multiclass_nms
  from mmdet.models.losses import accuracy
  from mmdet.models.task_modules.samplers import SamplingResult
  from mmdet.models.utils import empty_instances, multi_apply
  from mmdet.registry import MODELS, TASK_UTILS
  from mmdet.structures.bbox import get_box_tensor, scale_boxes
  from mmdet.utils import ConfigType, InstanceList, OptMultiConfig
label_desc: Use image node_id 3 for calling the BBoxHead obj's underlying member method code with example usage: obj.custom_activation() and returns: getattr