code: def _fuse_bn_tensor(self, branch):
      if not branch:
          return 0, 0
      elif isinstance(branch, ConvBNLayer):
          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, BatchNorm2D)
          if not hasattr(self, "id_tensor"):
              input_dim = self.in_channels // self.groups
              kernel_value = paddle.zeros(
                  (
                      self.in_channels,
                      input_dim,
                      self.kernel_size,
                      self.kernel_size,
                  ),
                  dtype=branch.weight.dtype,
              )
              for i in range(self.in_channels):
                  kernel_value[
                      i,
                      i % input_dim,
                      self.kernel_size // 2,
                      self.kernel_size // 2,
                  ] = 1
              self.id_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: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import ParamAttr
  from paddle.nn.initializer import Constant, KaimingNormal
  from paddle.nn import AdaptiveAvgPool2D, BatchNorm2D, Conv2D, Dropout, Hardsigmoid, Hardswish, Identity, Linear, ReLU
  from paddle.regularizer import L2Decay
label_desc: Use image node_id 6 for calling the LearnableRepLayer obj's underlying member method code with example usage: obj._fuse_bn_tensor(branch) and returns: unknown,  unknown, int,  int