class_docstr: The temporal 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","temporal","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,
      kernel_size: tuple = (7, 1),
  ):
      super(TemporalBlock, self).__init__()
      self.conv = nn.Conv2d(
          in_channel, out_channel, kernel_size, bias=False
      )
      self.relu = nn.ReLU(inplace=True)
      self.bnf = nn.BatchNorm1d(out_channel)
      self.bnx = nn.BatchNorm1d(out_channel)
      self.bny = nn.BatchNorm1d(out_channel)
  
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 TemporalBlock object from inherited base classes: BaseModule with example: obj = TemporalBlock(in_channel, out_channel, kernel_size)