code: def backward(ctx, dout, *args):
      (
          x,
          norm_weight,
          norm_bias,
          linear_weight,
          mean,
          rstd,
      ) = ctx.saved_tensors
      dout = dout.reshape(-1, dout.shape[-1])
      dy = F.linear(dout, linear_weight.t())
      dlinear_bias = None if ctx.linear_bias_is_none else dout.sum(0)
      if dy.stride(-1) != 1:
          dy = dy.contiguous()
      assert dy.shape == x.shape
      if ctx.prenorm:
          dresidual = args[0]
          dresidual = dresidual.reshape(-1, dresidual.shape[-1])
          if dresidual.stride(-1) != 1:
              dresidual = dresidual.contiguous()
          assert dresidual.shape == x.shape
      else:
          dresidual = None
      dx, dnorm_weight, dnorm_bias, dresidual_in, y = _layer_norm_bwd(
          dy,
          x,
          norm_weight,
          norm_bias,
          ctx.eps,
          mean,
          rstd,
          dresidual,
          ctx.has_residual,
          ctx.is_rms_norm,
          x_dtype=ctx.x_dtype,
          recompute_output=True,
      )
      dlinear_weight = torch.einsum("bo,bi->oi", dout, y)
      return (
          dx.reshape(ctx.x_shape_og),
          dnorm_weight,
          dnorm_bias,
          dlinear_weight,
          dlinear_bias,
          dresidual_in.reshape(ctx.x_shape_og)
          if ctx.has_residual
          else None,
          None,
          None,
          None,
          None,
      )
  
imports: import math
  import torch
  import torch.nn.functional
  from torch.cuda.amp import custom_fwd, custom_bwd
  import triton
  import triton.language
label_desc: Use image node_id 2 for calling the LayerNormLinearFn obj's underlying member method code with example usage: obj.backward(ctx, dout) and returns: dx,  dnorm_weight,  dnorm_bias,  dlinear_weight,  dlinear_bias,  unknown,  None,  None,  None,  None