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 of various data for benchmarking input generation.
docstr_tok: ["Returns","a","dict","of","various","data","for","benchmarking","input","generation","."]
code: def model_inputs_data(self) -> Dict[str, Any]:
      """
      Returns a dict of various data for benchmarking input generation.
      """
      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 9 for calling the PredictFactory obj's underlying member method code with example usage: obj.model_inputs_data() and returns: dict