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.
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code: def __init__(
      self,
      in_channels,
      out_channels,
      kernel_size,
      stride,
      padding=(0, 0),
  ):
      super(SqnxtConv, self).__init__()
      self.conv = nn.Conv2d(
          in_channels=in_channels,
          out_channels=out_channels,
          kernel_size=kernel_size,
          stride=stride,
          padding=padding,
      )
      self.bn = nn.BatchNorm2d(num_features=out_channels)
      self.activ = nn.ReLU(inplace=True)
  
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 1 to create a new SqnxtConv object from inherited base classes: nn with example: obj = SqnxtConv(in_channels, out_channels, kernel_size, stride, padding)