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 __init__(self, image_size, in_channels, *args, **kwargs):
      super().__init__(image_size, in_channels * 2, *args, **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 1 to create a new SuperResModel object from inherited base classes: UNetModel with example: obj = SuperResModel(image_size, in_channels)