class_docstr: HorNet
  
  Args:
      in_chans (`int`): Number of input channels.
      num_classes (`int`): Number for classes for classification.
      depths (`tuple`): Model depths.
      base_dim (`int`): Base dimensions.
      drop_path_rate (`float`): Drop ratio of drop path.
      layer_scale_init_value (`float`): Initial value to scale layer.
      head_init_scale (`float`): Initial value to scale head.
      gnconv (`nn.Module`): gnConv layer(s).
      uniform_init (`bool`): Flag to control whether to apply the uniform initial weights.
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code: def forward_features(self, x):
      for i in range(4):
          x = self.downsample_layers[i](x)
          for blk in self.stages[i]:
              x = blk(x)
      return self.norm(x.mean([-2, -1]))
  
file_path: towhee/towhee/models/hornet/hornet.py
imports: import torch
  from torch import nn
  from towhee.models.utils.weight_init import trunc_normal_
  from towhee.models.utils import create_model
  from towhee.models.convnext.utils import LayerNorm
  from towhee.models.hornet import Block, GatedConv, get_configs
label_desc: Use image node_id 3 for calling the HorNet obj's underlying member method code with example usage: obj.forward_features(x) and returns: self