class_docstr: Creates a model (with already learned weights) to be used inference time.
class_docstr_tok: ["Creates","a","model","(","with","already","learned","weights",")","to","be","used","inference","time","."]
docstr: Returns a dict from qualname (method name) to QualNameMetadata. This is additional information for execution of specific methods of the model.
docstr_tok: ["Returns","a","dict","from","qualname","(","method","name",")","to","QualNameMetadata",".","This","is","additional","information","for","execution","of","specific","methods","of","the","model","."]
code: def qualname_metadata(self) -> Dict[str, QualNameMetadata]:
      """
      Returns a dict from qualname (method name) to QualNameMetadata. This is additional information for execution of specific methods of the model.
      """
      return {}
  
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 7 for calling the PredictFactory obj's underlying member method code with example usage: obj.qualname_metadata() and returns: dict