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 __init__(self, in_channels, out_channels):
      super(SqnxtInitBlock, self).__init__()
      self.conv = SqnxtConv(
          in_channels=in_channels,
          out_channels=out_channels,
          kernel_size=7,
          stride=2,
          padding=1,
      )
      self.pool = nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=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 SqnxtInitBlock object from inherited base classes: nn with example: obj = SqnxtInitBlock(in_channels, out_channels)