code: def forward(
      ctx: Any,
      input_: Tensor,
      E_x: Tensor,
      Var_x: Tensor,
      hidden_size: int,
      row_parallel_mode: ParallelMode,
      col_parallel_mode: ParallelMode,
  ) -> Tensor:
      input_ = input_ - E_x
      # in here, input = x - E[x], Var_x = 1 / sqrt(Var[x] + eps)
      ctx.normalized_shape = hidden_size
      output = input_ * Var_x
      ctx.save_for_backward(output, Var_x)
      ctx.row_parallel_mode = row_parallel_mode
      ctx.col_parallel_mode = col_parallel_mode
      return output
  
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 1 for calling the _Layernorm_2D obj's underlying member method code with example usage: obj.forward(ctx, input_, E_x, Var_x, hidden_size, row_parallel_mode, col_parallel_mode) and returns: output