class_docstr: SqueezeNext specific initial block.
  
  Parameters:
  ----------
  in_channels : int
      Number of input channels.
  out_channels : int
      Number of output channels.
class_docstr_tok: ["SqueezeNext","specific","initial","block",".","Parameters",":","--","--","--","--","--","in_channels",":","int","Number","of","input","channels",".","out_channels",":","int","Number","of","output","channels","."]
code: def forward(self, x):
      x = self.conv(x)
      x = self.pool(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 SqnxtInitBlock obj's underlying member method code with example usage: obj.forward(x) and returns: x