code: def residual_ref_implementation(
      vals, bias, res, gamma, beta, espilon, channels, dtype
  ):
      vals_f = vals.to(torch.float32)
      bias_f = bias.to(torch.float32).reshape(1, 1, -1)
      res_f = res.to(torch.float32)
      gamma_f = gamma.to(torch.float32)
      beta_f = beta.to(torch.float32)
      return torch.nn.functional.layer_norm(
          vals_f + bias_f + res_f,
          (channels,),
          weight=gamma_f,
          bias=beta_f,
      ).to(dtype)
  
file_path: H2O/h2o_flexgen/benchmark/third_party/DeepSpeed/tests/unit/ops/transformer/inference/test_layer_norm.py
imports: import deepspeed
  import torch
  import pytest
  from deepspeed.ops.op_builder import InferenceBuilder
label_desc: Use image node_id 5 for calling a global function with example usage: residual_ref_implementation(vals, bias, res, gamma, beta, espilon, channels, dtype) and returns: torch