code: def __init__(
      self,
      in_features: int,
      hidden_features: Optional[int] = None,
      out_features: Optional[int] = None,
      act_layer: Callable[..., nn.Module] = None,
      drop: float = 0.0,
      bias: bool = True,
  ) -> None:
      super().__init__()
      out_features = out_features or in_features
      hidden_features = hidden_features or in_features
      self.w12 = nn.Linear(in_features, 2 * hidden_features, bias=bias)
      self.w3 = nn.Linear(hidden_features, out_features, bias=bias)
  
file_path: dinov2/dinov2/layers/swiglu_ffn.py
imports: import os
  from typing import Callable, Optional
  import warnings
  from torch import Tensor, nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new SwiGLUFFN object from inherited base classes: nn with example: obj = SwiGLUFFN(in_features, hidden_features, out_features, act_layer, drop, bias)