code: def manual_rms_norm(input, normalized_shape, weight, eps):
      # layer norm should always be calculated in float32
      dims = tuple(i for i in range(-1, -len(normalized_shape) - 1, -1))
      variance = input.to(torch.float32).pow(2).mean(dims, keepdim=True)
      input = input * torch.rsqrt(variance + eps)
  
      if weight is None:
          return input
  
      # convert into half-precision if necessary
      if weight.dtype in [torch.float16, torch.bfloat16]:
          input = input.to(weight.dtype)
  
      return weight * input
  
imports: import importlib
  import numbers
  import torch
  from torch.nn.parameter import Parameter
  from torch.nn import init
  from torch.nn import functional
  from apex._autocast_utils import _cast_if_autocast_enabled
label_desc: Use image node_id 1 for calling a global function with example usage: manual_rms_norm(input, normalized_shape, weight, eps) and returns: unknown, input