code: def ref_implementation(vals, gamma, beta, espilon, channels, dtype):
      vals_f = vals.to(torch.float32)
      gamma_f = gamma.to(torch.float32)
      beta_f = beta.to(torch.float32)
      return torch.nn.functional.layer_norm(
          vals_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 2 for calling a global function with example usage: ref_implementation(vals, gamma, beta, espilon, channels, dtype) and returns: torch