class_docstr: PreResNet specific convolution block, with pre-activation.
  
  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
      Padding value for convolution layer.
class_docstr_tok: ["PreResNet","specific","convolution","block",",","with","pre-activation",".","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","Padding","value","for","convolution","layer","."]
code: def __init__(
      self, in_channels, out_channels, kernel_size, stride, padding
  ):
      super(PreResConv, self).__init__()
      self.bn = nn.BatchNorm2d(num_features=in_channels)
      self.activ = nn.ReLU(inplace=True)
      self.conv = nn.Conv2d(
          in_channels=in_channels,
          out_channels=out_channels,
          kernel_size=kernel_size,
          stride=stride,
          padding=padding,
          bias=False,
      )
  
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 PreResConv object from inherited base classes: nn with example: obj = PreResConv(in_channels, out_channels, kernel_size, stride, padding)