code: def create_predict_module(self, world_size: int) -> torch.nn.Module:
      logging.basicConfig(level=logging.INFO)
      default_cuda_rank = 0
      device = torch.device("cuda", default_cuda_rank)
      torch.cuda.set_device(device)
  
      eb_configs = [
          EmbeddingBagConfig(
              name=f"t_{feature_name}",
              embedding_dim=self.model_config.embedding_dim,
              num_embeddings=self.model_config.num_embeddings_per_feature[
                  feature_idx
              ]
              if self.model_config.num_embeddings is None
              else self.model_config.num_embeddings,
              feature_names=[feature_name],
          )
          for feature_idx, feature_name in enumerate(
              self.model_config.id_list_features_keys
          )
      ]
      ebc = EmbeddingBagCollection(
          tables=eb_configs, device=torch.device("meta")
      )
  
      module = DLRMPredictModule(
          embedding_bag_collection=ebc,
          dense_in_features=self.model_config.dense_in_features,
          dense_arch_layer_sizes=self.model_config.dense_arch_layer_sizes,
          over_arch_layer_sizes=self.model_config.over_arch_layer_sizes,
          id_list_features_keys=self.model_config.id_list_features_keys,
          dense_device=device,
      )
  
      sharders = [
          QuantEmbeddingBagCollectionSharder(),
      ]
  
      constraints = {}
      for feature_name in self.model_config.id_list_features_keys:
          constraints[f"t_{feature_name}"] = ParameterConstraints(
              sharding_types=[ShardingType.TABLE_WISE.value],
              compute_kernels=[EmbeddingComputeKernel.QUANT.value],
          )
  
      module = quantize_embeddings(
          module, dtype=torch.qint8, inplace=True
      )
  
      plan = EmbeddingShardingPlanner(
          topology=Topology(
              world_size=world_size,
              compute_device="cuda",
              local_world_size=world_size,
          ),
          constraints=constraints,
      ).plan(module, sharders)
  
      return trec_dist.DistributedModelParallel(
          module=module,
          device=device,
          env=trec_dist.ShardingEnv.from_local(
              world_size, default_cuda_rank
          ),
          plan=plan,
          sharders=sharders,
          init_data_parallel=False,
      )
  
imports: import logging
  from dataclasses import dataclass
  from typing import Dict, List, Optional
  import torch
  import torchrec.distributed
  from torchrec.distributed.embedding_types import EmbeddingComputeKernel
  from torchrec.distributed.planner import EmbeddingShardingPlanner, ParameterConstraints, Topology
  from torchrec.distributed.quant_embeddingbag import QuantEmbeddingBagCollectionSharder
  from torchrec.distributed.types import ShardingType
  from torchrec.inference.model_packager import load_pickle_config
  from torchrec.inference.modules import PredictFactory, PredictModule, quantize_embeddings
  from torchrec.models.dlrm import DLRM
  from torchrec.modules.embedding_configs import EmbeddingBagConfig
  from torchrec.modules.embedding_modules import EmbeddingBagCollection
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
label_desc: Use image node_id 2 for calling the DLRMPredictFactory obj's underlying member method code with example usage: obj.create_predict_module(world_size) and returns: trec_dist