class_docstr: A Module that encapsulates the call to :attr:`xformers.ops.swiglu`,
  and holds the weights for the 3 linear layers
class_docstr_tok: ["A","Module","that","encapsulates","the","call","to",":","attr",":","`","xformers.ops.swiglu","`",",","and","holds","the","weights","for","the","3","linear","layers"]
docstr: Used for testing - returns ordered arguments for operators
docstr_tok: ["Used","for","testing","-","returns","ordered","arguments","for","operators"]
code: def _ordered_params(
      self,
  ) -> Tuple[
      torch.Tensor,
      Optional[torch.Tensor],
      torch.Tensor,
      Optional[torch.Tensor],
      torch.Tensor,
      Optional[torch.Tensor],
  ]:
      """Used for testing - returns ordered arguments for operators"""
      b1: Optional[torch.Tensor]
      b2: Optional[torch.Tensor]
      if self.w12 is not None:
          w1w2 = self.w12.weight
          b1b2 = self.w12.bias
          w1, w2 = unbind(
              w1w2.view([2, w1w2.shape[0] // 2, w1w2.shape[1]]),
              dim=0,
          )
          if b1b2 is not None:
              b1, b2 = unbind(b1b2.view([2, b1b2.shape[0] // 2]), dim=0)
          else:
              b1, b2 = None, None
      else:
          w1, w2 = self.w1.weight, self.w2.weight
          b1, b2 = self.w1.bias, self.w2.bias
      return (
          w1,
          b1,
          w2,
          b2,
          self.w3.weight,
          self.w3.bias,
      )
  
imports: from dataclasses import dataclass
  from typing import Dict, Optional, Sequence, Tuple, Union
  import torch
  import torch.nn.functional
  from torch import nn
  from .common import BaseOperator, get_xformers_operator, register_operator
  from .unbind import stack_or_none, unbind
label_desc: Use image node_id 3 for calling the SwiGLU obj's underlying member method code with example usage: obj._ordered_params() and returns: w1,  b1,  w2,  b2,  self,  self