code: def preres_conv1x1(in_channels, out_channels, stride):
      """
      1x1 version of the PreResNet specific convolution block.
  
      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.
      """
      return PreResConv(
          in_channels=in_channels,
          out_channels=out_channels,
          kernel_size=1,
          stride=stride,
          padding=0,
      )
  
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 a global function with example usage: preres_conv1x1(in_channels, out_channels, stride) and returns: PreResConv