class_docstr: A UNetModel which can perform both upsampling and inpainting.
code: def __init__(self, *args, **kwargs):
      if "in_channels" in kwargs:
          kwargs = dict(kwargs)
          kwargs["in_channels"] = kwargs["in_channels"] * 3 + 1
      else:
          # Curse you, Python. Or really, just curse positional arguments :|.
          args = list(args)
          args[1] = args[1] * 3 + 1
      super().__init__(*args, **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 1 to create a new SuperResInpaintUNetModel object from inherited base classes: UNetModel with example: obj = SuperResInpaintUNetModel()