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 already sharded model with allocated weights.
  state_dict() must match TransformModule.transform_state_dict().
  It assumes that torch.distributed.init_process_group was already called
  and will shard model according to torch.distributed.get_world_size().
docstr_tok: ["Returns","already","sharded","model","with","allocated","weights",".","state_dict","(",")","must","match","TransformModule.transform_state_dict","(",")",".","It","assumes","that","torch.distributed.init_process_group","was","already","called","and","will","shard","model","according","to","torch.distributed.get_world_size","(",")","."]
code: def create_predict_module(self) -> nn.Module:
      """
      Returns already sharded model with allocated weights.
      state_dict() must match TransformModule.transform_state_dict().
      It assumes that torch.distributed.init_process_group was already called
      and will shard model according to torch.distributed.get_world_size().
      """
      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 1 for calling the PredictFactory obj's underlying member method code with example usage: obj.create_predict_module() without return types