class_docstr: PreResNet pure pre-activation block without convolution layer. It's used by itself as the final block.
  
  Parameters:
  ----------
  in_channels : int
      Number of input channels.
class_docstr_tok: ["PreResNet","pure","pre-activation","block","without","convolution","layer",".","It","'s","used","by","itself","as","the","final","block",".","Parameters",":","--","--","--","--","--","in_channels",":","int","Number","of","input","channels","."]
code: def __init__(self, in_channels):
      super(PreResActivation, self).__init__()
      self.bn = nn.BatchNorm2d(num_features=in_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 PreResActivation object from inherited base classes: nn with example: obj = PreResActivation(in_channels)