class_docstr: The fusion block of AFLink model.
  
  Args:
      in_channel (int): the dimension of the input channels.
      out_channel (int): the dimension of the output channels.
class_docstr_tok: ["The","fusion","block","of","AFLink","model",".","Args",":","in_channel","(","int",")",":","the","dimension","of","the","input","channels",".","out_channel","(","int",")",":","the","dimension","of","the","output","channels","."]
code: def __init__(self, in_channel: int, out_channel: int):
      super(FusionBlock, self).__init__()
      self.conv = nn.Conv2d(in_channel, out_channel, (1, 3), bias=False)
      self.bn = nn.BatchNorm2d(out_channel)
      self.relu = nn.ReLU(inplace=True)
  
imports: from collections import defaultdict
  from typing import Tuple
  import numpy
  import torch
  from mmengine.model import BaseModule
  from mmengine.runner.checkpoint import load_checkpoint
  from scipy.optimize import linear_sum_assignment
  from torch import Tensor, nn
  from mmdet.registry import TASK_UTILS
label_desc: Use image node_id 1 to create a new FusionBlock object from inherited base classes: BaseModule with example: obj = FusionBlock(in_channel, out_channel)