class_docstr: The classifier of AFLink model.
  
  Args:
      in_channel (int): the dimension of the input channels.
class_docstr_tok: ["The","classifier","of","AFLink","model",".","Args",":","in_channel","(","int",")",":","the","dimension","of","the","input","channels","."]
code: def forward(self, x1: Tensor, x2: Tensor) -> Tensor:
      x = torch.cat((x1, x2), dim=1)
      x = self.fc1(x)
      x = self.relu(x)
      x = self.fc2(x)
      return x
  
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 2 for calling the Classifier obj's underlying member method code with example usage: obj.forward(x1, x2) and returns: x