code: def __init__(
      self,
      in_features,
      hidden_features=None,
      out_features=None,
      act_layer=nn.GELU,
      drop=0.0,
      group=8,
      spatial_conv=False,
  ):
      super().__init__()
      out_features = out_features or in_features
      hidden_features = hidden_features or in_features
      drop_probs = to_2tuple(drop)
  
      self.in_features = in_features
      self.out_features = out_features
      self.spatial_conv = spatial_conv
      if self.spatial_conv:
          if group < 2:  # net setting
              hidden_features = in_features * 5 // 6
          else:
              hidden_features = in_features * 2
      self.hidden_features = hidden_features
      self.group = group
      self.conv1 = nn.Conv2d(
          in_features,
          hidden_features,
          1,
          stride=1,
          padding=0,
          bias=False,
      )
      self.act1 = act_layer()
      self.drop1 = nn.Dropout(drop_probs[0])
      if self.spatial_conv:
          self.conv2 = nn.Conv2d(
              hidden_features,
              hidden_features,
              3,
              stride=1,
              padding=1,
              groups=self.group,
              bias=False,
          )
          self.act2 = act_layer()
      else:
          self.conv2 = None
          self.act2 = None
      self.conv3 = nn.Conv2d(
          hidden_features,
          out_features,
          1,
          stride=1,
          padding=0,
          bias=False,
      )
      self.drop3 = nn.Dropout(drop_probs[1])
  
imports: import torch
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import to_2tuple, trunc_normal_, DropPath, PatchEmbed, LayerNorm2d, create_classifier, use_fused_attn
  from ._builder import build_model_with_cfg
  from ._manipulate import checkpoint_seq
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 1 to create a new SpatialMlp object from inherited base classes: nn with example: obj = SpatialMlp(in_features, hidden_features, out_features, act_layer, drop, group, spatial_conv)