class_docstr: Dispatcher to automatically select
  the best operator in :attr:`xformers.ops.swiglu`
class_docstr_tok: ["Dispatcher","to","automatically","select","the","best","operator","in",":","attr",":","`","xformers.ops.swiglu","`"]
code: def from_arguments(
      x: torch.Tensor,
      w1: torch.Tensor,
      b1: Optional[torch.Tensor],
      w2: torch.Tensor,
      b2: Optional[torch.Tensor],
      w3: torch.Tensor,
      b3: Optional[torch.Tensor],
  ) -> "SwiGLUOpDispatch":
      return SwiGLUOpDispatch(
          device=x.device,
          dtype=x.dtype,
          packed_weights=stack_or_none((w1, w2), dim=0) is not None,
          dtype_autocast_gpu=torch.get_autocast_gpu_dtype()
          if torch.is_autocast_enabled()
          else w1.dtype,
          bias_enabled=b1 is not None
          and b2 is not None
          and b3 is not None,
      )
  
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 SwiGLUOpDispatch obj's underlying member method code with example usage: obj.from_arguments(x, w1, b1, w2, b2, w3, b3) and returns: SwiGLUOpDispatch