class_docstr: UpsampleConvLayer
  Upsamples the input and then does a convolution. This method gives better results
  compared to ConvTranspose2d.
  ref: http://distill.pub/2016/deconv-checkerboard/
class_docstr_tok: ["UpsampleConvLayer","Upsamples","the","input","and","then","does","a","convolution",".","This","method","gives","better","results","compared","to","ConvTranspose2d",".","ref",":","http",":","\/\/distill.pub\/2016\/deconv-checkerboard\/"]
code: def forward(self, x):
      x_in = x
      if self.upsample:
          x_in = torch.nn.functional.interpolate(
              x_in, mode="nearest", scale_factor=self.upsample
          )
      out = self.reflection_pad(x_in)
      out = self.conv2d(out)
      return out
  
file_path: ignite/examples/fast_neural_style/transformer_net.py
imports: import torch
label_desc: Use image node_id 2 for calling the UpsampleConvLayer obj's underlying member method code with example usage: obj.forward(x) and returns: out