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=device)
  
      module = DLRMPredictSingleGPUModule(
          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,
      )
  
      module = quantize_embeddings(
          module, dtype=torch.qint8, inplace=True
      )
      # TensorRT Lowering - Use torch_tensorrt.fx (https://github.com/pytorch/TensorRT) for lowering dense module
  
      # Follow https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/example/fx2trt_example.py
      # for fully detailed example on splitting and lowering a submodule.
  
      # Example for lowering the dense part of this DLRMPredictSingleGPUModule:
  
      # import torch.fx
      # import torch_tensorrt.fx.tracer.acc_tracer.acc_tracer as acc_tracer
      # from torch_tensorrt.fx.tools.trt_splitter import TRTSplitter
  
      # sample_input = {
      # "float_features": torch.ones(13),
      # "id_list_features.lengths": torch.ones(26),
      # "id_list_features.values": torch.ones(26)
      # }
  
      # traced = acc_tracer.trace(model, sample_input)
      # splitter = TRTSplitter(traced, sample_input)
      # split_mod = splitter()
  
      # Lower dense part (_run_on_acc_0, the part that can be lowered)
      # interp = TRTInterpreter(split_mod._run_on_acc_0, InputTensorSpec.from_tensors(inputs))
      # r = interp.run()
      # trt_mod = TRTModule(r.engine, r.input_names, r.output_names)
      # split_mod._run_on_acc_0 = trt_mod
      # return split_mod
  
      return module
  
file_path: torchrec/examples/inference/dlrm_predict_single_gpu.py
imports: import logging
  from typing import Dict
  import torch
  from dlrm_predict import DLRMModelConfig, DLRMPredictModule
  from torchrec.inference.model_packager import load_pickle_config
  from torchrec.inference.modules import PredictFactory, quantize_embeddings
  from torchrec.modules.embedding_configs import EmbeddingBagConfig
  from torchrec.modules.embedding_modules import EmbeddingBagCollection
label_desc: Use image node_id 2 for calling the DLRMPredictSingleGPUFactory obj's underlying member method code with example usage: obj.create_predict_module(world_size) and returns: module