code: def forward(self, input, residual, residual_norm, bias):
      if self.inter_w is None:
          self._inter_w, self._inter_b = self._merge_inter_w()
      else:
          self._inter_w = self.inter_w
          self._inter_b = self.inter_b
  
      residual_add = None
      if self.attn_nw is None:
          output = self.fused_gemm_gelu(
              input=residual_norm,
              weight=self._inter_w,
              bias=self._inter_b,
              weight_out=self.output_w,
          )
      else:
          output, residual_add = self.mlp_gemm_func(
              input=input,
              residual=residual,
              weight_interm=self._inter_w,
              weight_out=self.output_w,
              input_bias=bias,
              bias=self._inter_b,
              gamma=self.attn_nw,
              beta=self.attn_nb,
          )
  
      residual = self.residual_add_func(
          hidden_state=output,
          residual=residual,
          add_bias=bias is not None,
          attention_output=input,
          attention_bias=bias if bias is not None else self.output_b,
          final_bias=self.output_b,
          residual_add=residual_add,
      )
      if (
          self.mp_group is not None
          and dist.get_world_size(group=self.mp_group) > 1
      ):
          dist.all_reduce(residual, group=self.mp_group)
  
      return residual
  
file_path: DeepSpeed/deepspeed/ops/transformer/inference/ds_mlp.py
imports: import math
  import torch
  import torch.nn
  from deepspeed import comm
  from deepspeed.utils.types import GATED_ACTIVATION_TYPES
  from deepspeed.accelerator import get_accelerator
  from .op_binding import MLPGemmOp, VectorMatMulOp, GELUGemmOp, ResidualAddOp
label_desc: Use image node_id 3 for calling the DeepSpeedMLP obj's underlying member method code with example usage: obj.forward(input, residual, residual_norm, bias) and returns: residual