class_docstr: The roi embed head.
  
  This module is used in multi-object tracking methods, such as MaskTrack
  R-CNN.
  
  Args:
      num_convs (int): The number of convoluational layers to embed roi
          features. Defaults to 0.
      num_fcs (int): The number of fully connection layers to embed roi
          features. Defaults to 0.
      roi_feat_size (int|tuple(int)): The spatial size of roi features.
          Defaults to 7.
      in_channels (int): The input channel of roi features. Defaults to 256.
      conv_out_channels (int): The output channel of roi features after
          forwarding convoluational layers. Defaults to 256.
      with_avg_pool (bool): Whether use average pooling before passing roi
          features into fully connection layers. Defaults to False.
      fc_out_channels (int): The output channel of roi features after
          forwarding fully connection layers. Defaults to 1024.
      conv_cfg (dict): Config dict for convolution layer. Defaults to None,
          which means using conv2d.
      norm_cfg (dict): Config dict for normalization layer. Defaults to None.
      loss_match (dict): The loss function. Defaults to
          dict(type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)
      init_cfg (dict): Configuration of initialization. Defaults to None.
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code: def custom_activation(self):
      return getattr(self.loss_match, "custom_activation", False)
  
imports: from collections import defaultdict
  from typing import List, Optional, Tuple
  import torch
  import torch.nn
  from mmcv.cnn import ConvModule
  from mmengine.model import BaseModule
  from torch import Tensor
  from torch.nn.modules.utils import _pair
  from mmdet.models.losses import accuracy
  from mmdet.models.task_modules import SamplingResult
  from mmdet.models.task_modules.tracking import embed_similarity
  from mmdet.registry import MODELS
label_desc: Use image node_id 3 for calling the RoIEmbedHead obj's underlying member method code with example usage: obj.custom_activation() and returns: getattr