code: def _fuse_bn_tensor(conv, bn, in_channels=None, device=None):
      in_channels = (
          in_channels if in_channels else bn.running_mean.shape[0]
      )
      device = device if device else bn.weight.device
      if isinstance(conv, nn.Conv2d):
          kernel = conv.weight
          assert conv.bias is None
      else:
          assert isinstance(conv, nn.Identity)
          kernel = torch.ones(in_channels, 1, 1, 1, device=device)
  
      if isinstance(bn, nn.BatchNorm2d):
          running_mean = bn.running_mean
          running_var = bn.running_var
          gamma = bn.weight
          beta = bn.bias
          eps = bn.eps
          std = (running_var + eps).sqrt()
          t = (gamma / std).reshape(-1, 1, 1, 1)
          return kernel * t, beta - running_mean * gamma / std
      assert isinstance(bn, nn.Identity)
      return kernel, torch.zeros(in_channels).to(kernel.device)
  
imports: import copy
  from functools import partial
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import SelectAdaptivePool2d, Linear, make_divisible
  from ._builder import build_model_with_cfg
  from ._efficientnet_blocks import SqueezeExcite, ConvBnAct
  from ._manipulate import checkpoint_seq
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 5 for calling the RepGhostModule obj's underlying member method code with example usage: obj._fuse_bn_tensor(conv, bn, in_channels, device) and returns: kernel,  torch, unknown,  unknown