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 forward(self, x):
      x = self.features(x)
      x = x.view(x.size(0), -1)
      x = self.output(x)
      return x
  
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 3 for calling the SqueezeNext obj's underlying member method code with example usage: obj.forward(x) and returns: x