code: def test_rmsnorm(self):
      class T5LayerNorm(nn.Module):
          def __init__(self, hidden_size, eps=1e-6):
              """
              Construct a layernorm module in the T5 style No bias and no subtraction of mean.
              """
              super().__init__()
              self.weight = nn.Parameter(torch.ones(hidden_size))
              self.variance_epsilon = eps
  
          def forward(self, hidden_states):
              # layer norm should always be calculated in float32
              variance = (
                  hidden_states.to(torch.float32)
                  .pow(2)
                  .mean(-1, keepdim=True)
              )
              hidden_states = hidden_states * torch.rsqrt(
                  variance + self.variance_epsilon
              )
  
              # convert into half-precision if necessary
              if self.weight.dtype in [torch.float16, torch.bfloat16]:
                  hidden_states = hidden_states.to(self.weight.dtype)
  
              return self.weight * hidden_states
  
      bs = 256
      seq = 256
      hidden = 1024
      t5_norm = T5LayerNorm(hidden)
      t5_norm_inputs = [(bs, seq, hidden)]
      run_and_compare_activation(self, t5_norm, t5_norm_inputs)
  
imports: import torch
  import torch.nn
  import torch.fx
  from functorch import make_fx
  from torch.nn import functional
  from functorch.compile import memory_efficient_fusion
  from torch._functorch.compile_utils import fx_graph_cse
  from torch.testing._internal.common_utils import TestCase, run_tests
  import inspect
  import random
  from typing import Callable
  import unittest
label_desc: Use image node_id 7 for calling the TestMemoryEfficientOpAuthoring obj's underlying member method code with example usage: obj.test_rmsnorm() and returns: unknown