code: def forward(self, hidden_states):
      variance = (
          hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
      )
      hidden_states = hidden_states * torch.rsqrt(variance + self.eps)
      if self.weight.dtype in [torch.float16, torch.bfloat16]:
          hidden_states = hidden_states.to(self.weight.dtype)
  
      return hidden_states * self.weight
  
imports: import torch
  from deepspeed import comm
  from torch import nn
  from torch.nn import functional
  from torch.nn.parameter import Parameter
  from deepspeed.accelerator import get_accelerator
  from deepspeed.module_inject.tp_shard import get_shard_size, get_shard_size_list
label_desc: Use image node_id 2 for calling the RMSNormalize obj's underlying member method code with example usage: obj.forward(hidden_states) and returns: unknown