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 __init__(self, in_channels, out_channels, stride):
      super(SqnxtUnit, self).__init__()
      if stride == 2:
          reduction_den = 1
          self.resize_identity = True
      elif in_channels > out_channels:
          reduction_den = 4
          self.resize_identity = True
      else:
          reduction_den = 2
          self.resize_identity = False
  
      self.conv1 = SqnxtConv(
          in_channels=in_channels,
          out_channels=(in_channels // reduction_den),
          kernel_size=1,
          stride=stride,
      )
      self.conv2 = SqnxtConv(
          in_channels=(in_channels // reduction_den),
          out_channels=(in_channels // (2 * reduction_den)),
          kernel_size=1,
          stride=1,
      )
      self.conv3 = SqnxtConv(
          in_channels=(in_channels // (2 * reduction_den)),
          out_channels=(in_channels // reduction_den),
          kernel_size=(1, 3),
          stride=1,
          padding=(0, 1),
      )
      self.conv4 = SqnxtConv(
          in_channels=(in_channels // reduction_den),
          out_channels=(in_channels // reduction_den),
          kernel_size=(3, 1),
          stride=1,
          padding=(1, 0),
      )
      self.conv5 = SqnxtConv(
          in_channels=(in_channels // reduction_den),
          out_channels=out_channels,
          kernel_size=1,
          stride=1,
      )
  
      if self.resize_identity:
          self.identity_conv = SqnxtConv(
              in_channels=in_channels,
              out_channels=out_channels,
              kernel_size=1,
              stride=stride,
          )
      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 SqnxtUnit object from inherited base classes: nn with example: obj = SqnxtUnit(in_channels, out_channels, stride)