class_docstr: A UNetModel that performs super-resolution.
  
  Expects an extra kwarg `low_res` to condition on a low-resolution image.
class_docstr_tok: ["A","UNetModel","that","performs","super-resolution",".","Expects","an","extra","kwarg","`","low_res","`","to","condition","on","a","low-resolution","image","."]
code: def forward(self, x, timesteps, low_res=None, **kwargs):
      _, _, new_height, new_width = x.shape
      upsampled = F.interpolate(
          low_res, (new_height, new_width), mode="bilinear"
      )
      x = paddle.concat([x, upsampled], axis=1)
      return super().forward(x, timesteps, **kwargs)
  
imports: import math
  from abc import abstractmethod
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from .nn import avg_pool_nd
  from .nn import checkpoint
  from .nn import conv_nd
  from .nn import linear
  from .nn import normalization
  from .nn import SiLU
  from .nn import timestep_embedding
  from .nn import zero_module
label_desc: Use image node_id 2 for calling the SuperResModel obj's underlying member method code with example usage: obj.forward(x, timesteps, low_res) and returns: super