class_docstr: SqueezeNext unit.
  
  Parameters:
  ----------
  in_channels : int
      Number of input channels.
  out_channels : int
      Number of output channels.
  stride : int or tuple/list of 2 int
      Strides of the convolution.
class_docstr_tok: ["SqueezeNext","unit",".","Parameters",":","--","--","--","--","--","in_channels",":","int","Number","of","input","channels",".","out_channels",":","int","Number","of","output","channels",".","stride",":","int","or","tuple\/list","of","2","int","Strides","of","the","convolution","."]
code: def forward(self, x):
      if self.resize_identity:
          identity = self.identity_conv(x)
      else:
          identity = x
      identity = self.activ(identity)
      x = self.conv1(x)
      x = self.conv2(x)
      x = self.conv3(x)
      x = self.conv4(x)
      x = self.conv5(x)
      x = x + identity
      x = self.activ(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 2 for calling the SqnxtUnit obj's underlying member method code with example usage: obj.forward(x) and returns: x