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: Run transformations that depends on weights of the predict module. e.g. lowering to a backend.
docstr_tok: ["Run","transformations","that","depends","on","weights","of","the","predict","module",".","e.g",".","lowering","to","a","backend","."]
code: def run_weights_dependent_transformations(
      self, predict_module: torch.nn.Module
  ) -> torch.nn.Module:
      """
      Run transformations that depends on weights of the predict module. e.g. lowering to a backend.
      """
      pass
  
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 6 for calling the PredictFactory obj's underlying member method code with example usage: obj.run_weights_dependent_transformations(predict_module) without return types