class_docstr: Floating point post-LayerNorm kernel for CUDA/RoCM.
  
  Performs: z = ln(x + y)
class_docstr_tok: ["Floating","point","post-LayerNorm","kernel","for","CUDA\/RoCM",".","Performs",":","z","=","ln","(","x","+","y",")"]
docstr: Either input_x or input_y can alias output_z.
  
  Parameters:
      output_z (torch.Tensor): Output tensor.
      input_x (torch.Tensor): Input tensor.
      input_y (torch.Tensor): Input tensor.
      gamma (torch.Tensor): Gamma tensor.
      beta (torch.Tensor): Beta tensor.
  
  Returns:
      output (torch.Tensor): Output tensor.
docstr_tok: ["Either","input_x","or","input_y","can","alias","output_z",".","Parameters",":","output_z","(","torch.Tensor",")",":","Output","tensor",".","input_x","(","torch.Tensor",")",":","Input","tensor",".","input_y","(","torch.Tensor",")",":","Input","tensor",".","gamma","(","torch.Tensor",")",":","Gamma","tensor",".","beta","(","torch.Tensor",")",":","Beta","tensor",".","Returns",":","output","(","torch.Tensor",")",":","Output","tensor","."]
code: def __call__(
      self,
      output_z: torch.Tensor,
      input_x: torch.Tensor,
      input_y: torch.Tensor,
      gamma: torch.Tensor,
      beta: torch.Tensor,
  ) -> torch.Tensor:
      """
      Either input_x or input_y can alias output_z.
  
      Parameters:
          output_z (torch.Tensor): Output tensor.
          input_x (torch.Tensor): Input tensor.
          input_y (torch.Tensor): Input tensor.
          gamma (torch.Tensor): Gamma tensor.
          beta (torch.Tensor): Beta tensor.
  
      Returns:
          output (torch.Tensor): Output tensor.
      """
      self.inf_module.post_layer_norm(
          output_z, input_x, input_y, gamma, beta, self.epsilon
      )
      return output_z
  
imports: import torch
  from .cuda_fp_ln_base import CUDAFPLNBase
label_desc: Use image node_id 1 for calling the CUDAFPPostLN obj's underlying member method code with example usage: obj.__call__(output_z, input_x, input_y, gamma, beta) and returns: output_z