code: def backward(ctx: Any, output_grad: Tensor) -> Tuple[Tensor, ...]:
      A, B = ctx.saved_tensors
  
      with torch.no_grad():
          A_grad = torch.matmul(output_grad, B)
          A_grad = A_grad.reshape(ctx.A_shape)
          B_grad = torch.matmul(
              output_grad.reshape(-1, output_grad.shape[-1]).transpose(
                  0, 1
              ),
              A,
          )
          B_grad = reduce_scatter(B_grad, -1, ctx.col_parallel_mode)
          B_grad = B_grad.reshape(ctx.B_shape)
          if ctx.use_bias:
              bias_grad = torch.sum(
                  output_grad, dim=tuple(range(output_grad.ndim - 1))
              )
              bias_grad = all_reduce(bias_grad, ctx.col_parallel_mode)
          else:
              bias_grad = None
  
      return (
          A_grad,
          B_grad,
          bias_grad,
          None,
          None,
          None,
          None,
          None,
          None,
          None,
          None,
          None,
          None,
      )
  
imports: from typing import Any, Optional, Tuple
  import torch
  import torch.distributed
  from torch import Tensor
  from torch.cuda.amp import custom_bwd, custom_fwd
  from colossalai.legacy.communication.collective import all_gather, all_reduce, reduce_scatter
  from colossalai.legacy.context.parallel_mode import ParallelMode
  from colossalai.legacy.core import global_context
  from colossalai.utils import get_current_device
label_desc: Use image node_id 2 for calling the _Classifier2D obj's underlying member method code with example usage: obj.backward(ctx, output_grad) and returns: A_grad,  B_grad,  bias_grad,  None,  None,  None,  None,  None,  None,  None,  None,  None,  None