class_docstr: SqueezeNext specific convolution block.
  
  Parameters:
  ----------
  in_channels : int
      Number of input channels.
  out_channels : int
      Number of output channels.
  kernel_size : int or tuple/list of 2 int
      Convolution window size.
  stride : int or tuple/list of 2 int
      Strides of the convolution.
  padding : int or tuple/list of 2 int, default (0, 0)
      Padding value for convolution layer.
class_docstr_tok: ["SqueezeNext","specific","convolution","block",".","Parameters",":","--","--","--","--","--","in_channels",":","int","Number","of","input","channels",".","out_channels",":","int","Number","of","output","channels",".","kernel_size",":","int","or","tuple\/list","of","2","int","Convolution","window","size",".","stride",":","int","or","tuple\/list","of","2","int","Strides","of","the","convolution",".","padding",":","int","or","tuple\/list","of","2","int",",","default","(","0",",","0",")","Padding","value","for","convolution","layer","."]
code: def forward(self, x):
      x = self.conv(x)
      x = self.bn(x)
      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 SqnxtConv obj's underlying member method code with example usage: obj.forward(x) and returns: x