class_docstr: SqueezeNext model from 'SqueezeNext: Hardware-Aware Neural Network Design,' https://arxiv.org/abs/1803.10615.
  
  Parameters:
  ----------
  channels : list of list of int
      Number of output channels for each unit.
  init_block_channels : int
      Number of output channels for the initial unit.
  final_block_channels : int
      Number of output channels for the final block of the feature extractor.
  in_channels : int, default 3
      Number of input channels.
  num_classes : int, default 1000
      Number of classification classes.
class_docstr_tok: ["SqueezeNext","model","from","'SqueezeNext",":","Hardware-Aware","Neural","Network","Design",",","'","https",":","\/\/arxiv.org\/abs\/1803.10615",".","Parameters",":","--","--","--","--","--","channels",":","list","of","list","of","int","Number","of","output","channels","for","each","unit",".","init_block_channels",":","int","Number","of","output","channels","for","the","initial","unit",".","final_block_channels",":","int","Number","of","output","channels","for","the","final","block","of","the","feature","extractor",".","in_channels",":","int",",","default","3","Number","of","input","channels",".","num_classes",":","int",",","default","1000","Number","of","classification","classes","."]
code: def _init_params(self):
      for name, module in self.named_modules():
          if isinstance(module, nn.Conv2d):
              init.kaiming_uniform_(module.weight)
              if module.bias is not None:
                  init.constant_(module.bias, 0)
  
imports: import numpy
  import torch
  from torch.autograd import Variable
  from pytorch2keras.converter import pytorch_to_keras
  import os
  import torch.nn
  import torch.nn.init
label_desc: Use image node_id 2 for calling the SqueezeNext obj's underlying member method code with example usage: obj._init_params() without return types