class_docstr: ConvNeXt
      A Pypaddle 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
      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.
class_docstr_tok: ["ConvNeXt","A","Pypaddle","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","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","."]
code: def forward(self, x):
      x = self.forward_features(x["image"])
      return x
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import ParamAttr
  from paddle.nn.initializer import Constant
  import numpy
  from ppdet.core.workspace import register, serializable
  from ..shape_spec import ShapeSpec
  from .transformer_utils import DropPath, trunc_normal_, zeros_
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