text: from torch2trt.torch2trt import *
  
  
  @tensorrt_converter('torch.nn.BatchNorm2d.forward')
  def convert_BatchNorm2d(ctx):
      module = ctx.method_args[0]
      input = ctx.method_args[1]
      input_trt = trt_(ctx.network, input)
      output = ctx.method_return
      
      scale = module.weight.detach().cpu().numpy() / np.sqrt(module.running_var.detach().cpu().numpy() + module.eps)
      bias = module.bias.detach().cpu().numpy() - module.running_mean.detach().cpu().numpy() * scale
      power = np.ones_like(scale)
  
      layer = ctx.network.add_scale(input_trt, trt.ScaleMode.CHANNEL, bias, scale, power)    
  
  
      output._trt = layer.get_output(0)