class_docstr: 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 = th.cat([x, upsampled], dim=1)
      return super().forward(x, timesteps, **kwargs)
  
imports: import math
  from abc import abstractmethod
  import torch
  import torch.nn
  import torch.nn.functional
  from .fp16_util import convert_module_to_f16, convert_module_to_f32
  from .nn import avg_pool_nd, conv_nd, linear, normalization, timestep_embedding, zero_module
label_desc: Use image node_id 2 for calling the SuperResUNetModel obj's underlying member method code with example usage: obj.forward(x, timesteps, low_res) and returns: super