class_docstr: Base class for hyperprior autoencoder.
  
  This base class wraps a number of utility functions for working with
  Balle-style compression autoencoders implemented in the style of
  CompressAI, such as parameter collection and device management. Users are
  expected to subclass this calss before writing the appropriate PyTorch
  operations.
class_docstr_tok: ["Base","class","for","hyperprior","autoencoder",".","This","base","class","wraps","a","number","of","utility","functions","for","working","with","Balle-style","compression","autoencoders","implemented","in","the","style","of","CompressAI",",","such","as","parameter","collection","and","device","management",".","Users","are","expected","to","subclass","this","calss","before","writing","the","appropriate","PyTorch","operations","."]
code: def decompress(
      self,
      compressed_data: HyperpriorCompressedOutput,
      force_cpu: bool = True,
  ) -> Tensor:
      raise NotImplementedError
  
imports: from typing import List, Optional, Tuple
  import torch
  import torch.nn
  from compressai.entropy_models import EntropyBottleneck
  from compressai.models import CompressionModel
  from compressai.ops import quantize_ste
  from torch import Tensor
  from neuralcompression import HyperpriorCompressedOutput, HyperpriorOutput
label_desc: Use image node_id 11 for calling the _HyperpriorAutoencoderBase obj's underlying member method code with example usage: obj.decompress(compressed_data, force_cpu) without return types