class_docstr: ConvNeXt
      A PyTorch impl of : `A ConvNet for the 2020s`  -
        https://arxiv.org/pdf/2201.03545.pdf
  
  Args:
      depths: Number of blocks at each stage. Default: [3, 3, 9, 3]
      dims: Feature dimension at each stage. Default: [96, 192, 384, 768]
      in_chans: Number of input image channels. Default: 3
      drop_path_rate: Stochastic depth rate. Default: 0.
      layer_scale_init_value: Init value for Layer Scale. Default: 1e-6.
class_docstr_tok: ["ConvNeXt","A","PyTorch","impl","of",":","`","A","ConvNet","for","the","2020s","`","-","https",":","\/\/arxiv.org\/pdf\/2201.03545.pdf","Args",":","depths",":","Number","of","blocks","at","each","stage",".","Default",":","[","3",",","3",",","9",",","3","]","dims",":","Feature","dimension","at","each","stage",".","Default",":","[","96",",","192",",","384",",","768","]","in_chans",":","Number","of","input","image","channels",".","Default",":","3","drop_path_rate",":","Stochastic","depth","rate",".","Default",":","0.","layer_scale_init_value",":","Init","value","for","Layer","Scale",".","Default",":","1e-6","."]
code: def _init_weights(self, m):
      if isinstance(m, (nn.Conv2d, nn.Linear)):
          trunc_normal_(m.weight, std=0.02)
          nn.init.constant_(m.bias, 0)
  
imports: from typing import List
  import torch
  import torch.nn
  import torch.nn.functional
  from vissl.config import AttrDict
  from vissl.models.model_helpers import DropPath, trunc_normal_
  from vissl.models.trunks import register_model_trunk
label_desc: Use image node_id 2 for calling the ConvNeXt obj's underlying member method code with example usage: obj._init_weights(m) without return types