class_docstr: Model wrapper for Named Entity Recognition
class_docstr_tok: ["Model","wrapper","for","Named","Entity","Recognition"]
code: def parallel_preparation_inference(
      self,
  ):
      if torch.cuda.device_count() == 1:
          self.model = self.model.cuda()
          return
      # prepare the model for parallel inference
      if self.model.action_head[0].weight.get_device() != self.device:
          logger.info(
              f"Moving model from {self.model.action_head.weight.get_device()} to {self.device} for inference"
          )
          self.model.lr_scorer = self.model.lr_scorer.to(self.device)
          self.model.action_head = self.model.action_head.to(
              self.device
          )
  
          if hasattr(self.model, "emb_l_distance"):
              self.model.emb_l_distance = self.model.emb_l_distance.to(
                  self.device
              )
          if not self.model.t5.model_parallel:
              self.model.t5.parallelize()
          torch.cuda.empty_cache()
      return
  
file_path: DeepKE/src/deepke/triple_extraction/ASP/models/model_ner.py
imports: import sys
  import os
  import torch
  import torch.nn
  import logging
  from transformers import T5Tokenizer
  from .t5_ner import T5NER
label_desc: Use image node_id 3 for calling the NERModel obj's underlying member method code with example usage: obj.parallel_preparation_inference() without return types