class_docstr: ConvNeXt
      A PyTorch impl of : `A ConvNet for the 2020s`  -
        https://arxiv.org/pdf/2201.03545.pdf
  Args:
      in_chans (int): Number of input image channels. Default: 3
      num_classes (int): Number of classes for classification head. Default: 1000
      depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3]
      dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768]
      drop_path_rate (float): Stochastic depth rate. Default: 0.
      layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
      head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1.
class_docstr_tok: ["ConvNeXt","A","PyTorch","impl","of",":","`","A","ConvNet","for","the","2020s","`","-","https",":","\/\/arxiv.org\/pdf\/2201.03545.pdf","Args",":","in_chans","(","int",")",":","Number","of","input","image","channels",".","Default",":","3","num_classes","(","int",")",":","Number","of","classes","for","classification","head",".","Default",":","1000","depths","(","tuple","(","int",")",")",":","Number","of","blocks","at","each","stage",".","Default",":","[","3",",","3",",","9",",","3","]","dims","(","int",")",":","Feature","dimension","at","each","stage",".","Default",":","[","96",",","192",",","384",",","768","]","drop_path_rate","(","float",")",":","Stochastic","depth","rate",".","Default",":","0.","layer_scale_init_value","(","float",")",":","Init","value","for","Layer","Scale",".","Default",":","1e-6",".","head_init_scale","(","float",")",":","Init","scaling","value","for","classifier","weights","and","biases",".","Default",":","1","."]
code: def forward_features(self, x: torch.Tensor) -> torch.Tensor:
      for i in range(4):
          x = self.downsample_layers[i](x)
          x = self.stages[i](x)
  
      return self.norm(x.mean([-2, -1]))
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 3 for calling the ConvNeXt obj's underlying member method code with example usage: obj.forward_features(x) and returns: self