class_docstr: Floating point pre-LayerNorm kernel for CUDA/RoCM.
  
  Performs: z_res = x_res + y_hid
            z_hid = ln(z_hid)
class_docstr_tok: ["Floating","point","pre-LayerNorm","kernel","for","CUDA\/RoCM",".","Performs",":","z_res","=","x_res","+","y_hid","z_hid","=","ln","(","z_hid",")"]
docstr: z_res can alias x_res. All non-parameter input/output tensors
  must have the same shape. z_hid can alias y_hid.
  
  Parameters:
      z_res (torch.Tensor): Output residual.
      z_hid (torch.Tensor): Output hidden states.
      x_res (torch.Tensor): Input residual.
      y_hid (torch.Tensor): Input hidden states.
      gamma (torch.Tensor): Gamma tensor.
      beta (torch.Tensor): Beta tensor.
  
  Returns:
      output (torch.Tensor): Output tensor.
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code: def __call__(
      self,
      z_res: torch.Tensor,
      z_hid: torch.Tensor,
      x_res: torch.Tensor,
      y_hid: torch.Tensor,
      gamma: torch.Tensor,
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      """
      z_res can alias x_res. All non-parameter input/output tensors
      must have the same shape. z_hid can alias y_hid.
  
      Parameters:
          z_res (torch.Tensor): Output residual.
          z_hid (torch.Tensor): Output hidden states.
          x_res (torch.Tensor): Input residual.
          y_hid (torch.Tensor): Input hidden states.
          gamma (torch.Tensor): Gamma tensor.
          beta (torch.Tensor): Beta tensor.
  
      Returns:
          output (torch.Tensor): Output tensor.
      """
      self.inf_module.rms_pre_norm(
          z_hid, z_res, y_hid, x_res, gamma, self.epsilon
      )
      return z_res, z_hid
  
imports: from typing import Tuple
  import torch
  from .rms_norm_base import CUDARMSNormBase
label_desc: Use image node_id 1 for calling the CUDARMSPreNorm obj's underlying member method code with example usage: obj.__call__(z_res, z_hid, x_res, y_hid, gamma) and returns: z_res,  z_hid