class_docstr: ConvNeXt model
  
  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","model","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(self, x):
      x = self.forward_features(x)
      x = self.head(x)
      return x
  
file_path: towhee/towhee/models/convnext/convnext.py
imports: import torch
  from torch import nn
  from towhee.models.convnext.utils import LayerNorm, Block
  from towhee.models.convnext.configs import get_configs
  from towhee.models.utils.weight_init import trunc_normal_
  from towhee.models.utils import create_model
label_desc: Use image node_id 4 for calling the ConvNeXt obj's underlying member method code with example usage: obj.forward(x) and returns: x