class_docstr: PreResNet model from 'Identity Mappings in Deep Residual Networks,' https://arxiv.org/abs/1603.05027.
  
  Parameters:
  ----------
  channels : list of list of int
      Number of output channels for each unit.
  init_block_channels : int
      Number of output channels for the initial unit.
  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 in units.
  in_channels : int, default 3
      Number of input channels.
  num_classes : int, default 1000
      Number of classification classes.
class_docstr_tok: ["PreResNet","model","from","'Identity","Mappings","in","Deep","Residual","Networks",",","'","https",":","\/\/arxiv.org\/abs\/1603.05027",".","Parameters",":","--","--","--","--","--","channels",":","list","of","list","of","int","Number","of","output","channels","for","each","unit",".","init_block_channels",":","int","Number","of","output","channels","for","the","initial","unit",".","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","in","units",".","in_channels",":","int",",","default","3","Number","of","input","channels",".","num_classes",":","int",",","default","1000","Number","of","classification","classes","."]
code: def forward(self, x):
      x = self.features(x)
      x = x.view(int(x.size(0)), -1)
      x = self.output(x)
      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 3 for calling the PreResNet obj's underlying member method code with example usage: obj.forward(x) and returns: x