code: def backward(ctx, G_3D):
      # reshape input to [N_out * L, D]
      G = G_3D.reshape(-1, G_3D.size(-1))
      G_3D_sz = G_3D.size()
  
      grad_X = grad_W = grad_bias = None
  
      X_int8, state_X, W_int8, state_W = ctx.save_for_backward
      if ctx.needs_input_grad[1]:
          real_X = dequantize_rowwise(X_int8, state_X)
          del X_int8
          grad_W = torch.matmul(G.t(), real_X.to(G.dtype))
          del real_X
      if ctx.needs_input_grad[2]:
          grad_bias = G.sum(dim=0)
      if ctx.needs_input_grad[0]:
          G_int8, state_G = quantize_rowwise(G)
          del G
          W_int8 = W_int8.t().contiguous()
          grad_X = int8_matmul_mixed_dequantize(
              G_int8, W_int8.t(), state_G, state_W, None
          ).view(*G_3D_sz[:-1], -1)
  
      return grad_X, grad_W, grad_bias
  
imports: import torch
  import torch.nn
  import time
  from functools import partial
  from bitsandbytes.triton.triton_utils import is_triton_available
  from bitsandbytes.triton.dequantize_rowwise import dequantize_rowwise
  from bitsandbytes.triton.quantize_rowwise import quantize_rowwise
  from bitsandbytes.triton.quantize_columnwise_and_transpose import quantize_columnwise_and_transpose
  from bitsandbytes.triton.int8_matmul_rowwise_dequantize import int8_matmul_rowwise_dequantize
  from bitsandbytes.triton.quantize_global import quantize_global, quantize_global_transpose
  from bitsandbytes.triton.int8_matmul_mixed_dequantize import int8_matmul_mixed_dequantize
label_desc: Use image node_id 2 for calling the _switchback_global_mem_efficient obj's underlying member method code with example usage: obj.backward(ctx, G_3D) and returns: grad_X,  grad_W,  grad_bias