docstr: Initialize pretrained TAESD on the given device from the given checkpoints.
docstr_tok: ["Initialize","pretrained","TAESD","on","the","given","device","from","the","given","checkpoints","."]
code: def __init__(self, decoder_path="taesd_decoder.pth"):
      """Initialize pretrained TAESD on the given device from the given checkpoints."""
      super().__init__()
      self.decoder = decoder()
      self.decoder.load_state_dict(
          torch.load(
              decoder_path,
              map_location="cpu"
              if devices.device.type != "cuda"
              else None,
          )
      )
  
imports: import os
  import torch
  import torch.nn
  from modules import devices, paths_internal, shared
label_desc: Use image node_id 1 to create a new TAESDDecoder object from inherited base classes: nn with example: obj = TAESDDecoder(decoder_path)