code: def _fuse_bn_tensor(self, branch):
      if branch is None:
          return 0, 0
      if isinstance(branch, ConvBN):
          kernel = branch.conv.weight
          running_mean = branch.bn._mean
          running_var = branch.bn._variance
          gamma = branch.bn.weight
          beta = branch.bn.bias
          eps = branch.bn._epsilon
      else:
          assert isinstance(branch, nn.BatchNorm2D)
          if not hasattr(self, "id_tensor"):
              input_dim = self.in_channels // self.groups
              kernel_value = np.zeros(
                  (self.in_channels, input_dim, 3, 3), dtype=np.float32
              )
              for i in range(self.in_channels):
                  kernel_value[i, i % input_dim, 1, 1] = 1
              self.id_tensor = paddle.to_tensor(kernel_value)
          kernel = self.id_tensor
          running_mean = branch._mean
          running_var = branch._variance
          gamma = branch.weight
          beta = branch.bias
          eps = branch._epsilon
      std = (running_var + eps).sqrt()
      t = (gamma / std).reshape((-1, 1, 1, 1))
      return kernel * t, beta - running_mean * gamma / std
  
imports: import os
  from typing import Union
  import paddle
  import paddle.nn
  import paddlehub.vision.transforms
  import numpy
  from paddlehub.module.module import moduleinfo
  from paddlehub.module.cv_module import ImageClassifierModule
label_desc: Use image node_id 6 for calling the RepVGGBlock obj's underlying member method code with example usage: obj._fuse_bn_tensor(branch) and returns: unknown,  unknown, int,  int