code: def residual_store_ref_implementation(
      vals, bias, res, gamma, beta, epsilon, 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)
      res_output = vals_f + bias_f + res_f
      norm_output = torch.nn.functional.layer_norm(
          res_output,
          (channels,),
          weight=gamma_f,
          bias=beta_f,
          eps=epsilon,
      ).to(dtype)
      return norm_output, res_output.to(dtype)
  
file_path: DeepSpeed/tests/unit/ops/transformer/inference/test_layer_norm.py
imports: import deepspeed
  import torch
  import pytest
  from deepspeed.accelerator import get_accelerator
  from deepspeed.ops.op_builder import InferenceBuilder
  from .inference_test_utils import allclose, get_dtypes, assert_almost_equal
label_desc: Use image node_id 9 for calling a global function with example usage: residual_store_ref_implementation(vals, bias, res, gamma, beta, epsilon, channels, dtype) and returns: norm_output,  res_output