class_docstr: A Module that encapsulates the call to :attr:`xformers.ops.swiglu`,
  and holds the weights for the 3 linear layers
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docstr: Computes :attr:`swiglu` with the module's weights
  
  Args:
      x (torch.Tensor): A Tensor of shape ``[..., in_features]``
  
  Returns:
      torch.Tensor: A Tensor of shape ``[..., out_features]``
docstr_tok: ["Computes",":","attr",":","`","swiglu","`","with","the","module","'s","weights","Args",":","x","(","torch.Tensor",")",":","A","Tensor","of","shape","``","[","...",",","in_features","]","``","Returns",":","torch.Tensor",":","A","Tensor","of","shape","``","[","...",",","out_features","]","``"]
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      """Computes :attr:`swiglu` with the module's weights
  
      Args:
          x (torch.Tensor): A Tensor of shape ``[..., in_features]``
  
      Returns:
          torch.Tensor: A Tensor of shape ``[..., out_features]``
      """
      return swiglu(x, *self._ordered_params(), op=self.op)
  
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 2 for calling the SwiGLU obj's underlying member method code with example usage: obj.forward(x) and returns: swiglu