class_docstr: Floating point layer norm kernel for CUDA/RoCM.
  
  Performs: z = ln(x)
class_docstr_tok: ["Floating","point","layer","norm","kernel","for","CUDA\/RoCM",".","Performs",":","z","=","ln","(","x",")"]
docstr: output_z may alias input_x directly. All Tensors should have the same shape.
  
  Parameters:
      output_z (torch.Tensor): Output tensor.
      input_x (torch.Tensor): Input tensor.
      gamma (torch.Tensor): Gamma tensor.
docstr_tok: ["output_z","may","alias","input_x","directly",".","All","Tensors","should","have","the","same","shape",".","Parameters",":","output_z","(","torch.Tensor",")",":","Output","tensor",".","input_x","(","torch.Tensor",")",":","Input","tensor",".","gamma","(","torch.Tensor",")",":","Gamma","tensor","."]
code: def __call__(
      self,
      output_z: torch.Tensor,
      input_x: torch.Tensor,
      gamma: torch.Tensor,
  ) -> torch.Tensor:
      """
      output_z may alias input_x directly. All Tensors should have the same shape.
  
      Parameters:
          output_z (torch.Tensor): Output tensor.
          input_x (torch.Tensor): Input tensor.
          gamma (torch.Tensor): Gamma tensor.
      """
      self.inf_module.rms_norm(output_z, input_x, gamma, self.epsilon)
      return output_z
  
imports: import torch
  from .rms_norm_base import CUDARMSNormBase
label_desc: Use image node_id 1 for calling the CUDARMSNorm obj's underlying member method code with example usage: obj.__call__(output_z, input_x, gamma) and returns: output_z