class_docstr: Interface for modules to work in a torch.deploy based backend. Users should
  override predict_forward to convert batch input format to module input format.
  
  Call Args:
      batch: a dict of input tensors
  
  Returns:
      output: a dict of output tensors
  
  Args:
      module: the actual predict module
      device: the primary device for this module that will be used in forward calls.
  
  Example::
  
      module = PredictModule(torch.device("cuda", torch.cuda.current_device()))
class_docstr_tok: ["Interface","for","modules","to","work","in","a","torch.deploy","based","backend",".","Users","should","override","predict_forward","to","convert","batch","input","format","to","module","input","format",".","Call","Args",":","batch",":","a","dict","of","input","tensors","Returns",":","output",":","a","dict","of","output","tensors","Args",":","module",":","the","actual","predict","module","device",":","the","primary","device","for","this","module","that","will","be","used","in","forward","calls",".","Example",":",":","module","=","PredictModule","(","torch.device","(","``","cuda","''",",","torch.cuda.current_device","(",")",")",")"]
code: def state_dict(
      self,
      destination: Optional[Dict[str, Any]] = None,
      prefix: str = "",
      keep_vars: bool = False,
  ) -> Dict[str, Any]:
      # pyre-fixme[19]: Expected 0 positional arguments.
      return self._module.state_dict(destination, prefix, keep_vars)
  
imports: import abc
  import json
  from dataclasses import asdict, dataclass
  from typing import Any, Dict, List, Optional, Tuple, Type
  import torch
  import torch.nn
  import torch.quantization
  import torchrec
  import torchrec.quant
  from torchrec.modules.embedding_configs import QuantConfig
  from torchrec.modules.embedding_modules import EmbeddingBagCollectionInterface, EmbeddingCollectionInterface
label_desc: Use image node_id 5 for calling the PredictModule obj's underlying member method code with example usage: obj.state_dict(destination, prefix, keep_vars) and returns: self