class_docstr: PreResNet unit with residual connection.
  
  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.
  bottleneck : bool
      Whether to use a bottleneck or simple block in units.
  conv1_stride : bool
      Whether to use stride in the first or the second convolution layer of the block.
class_docstr_tok: ["PreResNet","unit","with","residual","connection",".","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",".","bottleneck",":","bool","Whether","to","use","a","bottleneck","or","simple","block","in","units",".","conv1_stride",":","bool","Whether","to","use","stride","in","the","first","or","the","second","convolution","layer","of","the","block","."]
code: def forward(self, x):
      identity = x
      x, x_pre_activ = self.body(x)
      if self.resize_identity:
          identity = self.identity_conv(x_pre_activ)
      x = x + identity
      return x
  
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 2 for calling the PreResUnit obj's underlying member method code with example usage: obj.forward(x) and returns: x