code: def forward(
      ctx,
      input,
      residual,
      residual_norm,
      bias,
      inter_w,
      inter_b,
      attn_nw,
      attn_nb,
      config,
      mp_group,
      output_b,
      output_w,
      q_scales,
      q_groups,
      merge_count,
      mlp_gemm_func,
      fused_gemm_gelu,
      vector_matmul_func,
      bias_residual_func,
      residual_add_func,
      activation_func_type=ActivationFuncType.GELU,
  ):
      if attn_nw is None:
          output = fused_gemm_gelu(
              residual_norm,
              inter_w,
              inter_w.scale,
              inter_b,
              output_w,
              output_w.scale,
              config.epsilon,
              config.pre_layer_norm,
              config.q_int8,
              False,
          )
      else:
          output, residual_add = mlp_gemm_func(
              input,
              residual,
              bias,
              inter_w,
              output_w,
              inter_b,
              attn_nw,
              attn_nb,
              config.epsilon,
              config.pre_layer_norm,
              config.mlp_after_attn,
              inter_w.scale,
              output_w.scale,
              config.q_int8,
              config.mlp_act_func_type,
          )
      residual = residual if config.pre_layer_norm else residual_add
      residual_add_func(
          output,  # hidden state
          residual,  # residual
          input,  # attention output
          bias if bias is not None else output_b,
          output_b,
          config.mp_size,  # model parallel size
          config.mlp_after_attn,  # whether mlp is after attention (GPTJ model architecture runs the MLP layer in parallel with attention)
          bias is not None,  # whether bias addition is fused
          config.pre_layer_norm,
      )  # whether the layer norm is applied before attention
      if (
          mp_group is not None
          and dist.get_world_size(group=mp_group) > 1
      ):
          dist.all_reduce(residual, group=mp_group)
      return residual
  
file_path: H2O/h2o_flexgen/benchmark/third_party/DeepSpeed/deepspeed/ops/transformer/inference/ds_mlp.py
imports: import torch
  from torch.autograd import Function
  from deepspeed.utils.types import ActivationFuncType
  from deepspeed import comm
  import torch.nn
  import math
  from ...None import op_builder
label_desc: Use image node_id 1 for calling the DeepSpeedMLPFunction obj's underlying member method code with example usage: obj.forward(ctx, input, residual, residual_norm, bias, inter_w, inter_b, attn_nw, attn_nb, config, mp_group, output_b, output_w, q_scales, q_groups, merge_count, mlp_gemm_func, fused_gemm_gelu, vector_matmul_func, bias_residual_func, residual_add_func, activation_func_type) and returns: residual