class_docstr: A text2im model 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",
          align_corners=False,
      )
      x = th.cat([x, upsampled], dim=1)
      return super().forward(x, timesteps, **kwargs)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from .nn import timestep_embedding
  from .unet import UNetModel
  from .xf import LayerNorm, Transformer, convert_module_to_f16
label_desc: Use image node_id 2 for calling the SuperResText2ImUNet obj's underlying member method code with example usage: obj.forward(x, timesteps, low_res) and returns: super