code: def _eager_functional_swiglu(
      x: torch.Tensor,
      w1: torch.Tensor,
      b1: torch.Tensor,
      w2: torch.Tensor,
      b2: torch.Tensor,
      w3: torch.Tensor,
      b3: torch.Tensor,
  ) -> torch.Tensor:
      x1 = F.linear(x, w1, b1)
      x2 = F.linear(x, w2, b2)
      hidden = F.silu(x1) * x2
      return F.linear(hidden, w3, b3)
  
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 1 for calling a global function with example usage: _eager_functional_swiglu(x, w1, b1, w2, b2, w3, b3) and returns: F