code: def __init__(self, in_channels, out_channels):
      super(ShuffleInitBlock, self).__init__()
  
      self.conv = nn.Conv2d(
          in_channels=in_channels,
          out_channels=out_channels,
          kernel_size=3,
          stride=2,
          padding=1,
          bias=False,
      )
      self.bn = nn.BatchNorm2d(num_features=out_channels)
      self.activ = nn.ReLU(inplace=True)
      self.pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
  
imports: import numpy
  from torch.autograd import Variable
  from pytorch2keras.converter import pytorch_to_keras
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.nn.init
label_desc: Use image node_id 1 to create a new ShuffleInitBlock object from inherited base classes: nn with example: obj = ShuffleInitBlock(in_channels, out_channels)