class_docstr: Model wrapper for coreference resolution.
class_docstr_tok: ["Model","wrapper","for","coreference","resolution","."]
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.emb_rr_distance.weight.get_device() != self.device:
          logger.info(
              f"Moving model from {self.model.emb_rr_distance.weight.get_device()} to {self.device} for inference"
          )
          self.model.rr_scorer = self.model.rr_scorer.to(self.device)
          self.model.lr_scorer = self.model.lr_scorer.to(self.device)
          self.model.action_head = self.model.action_head.to(
              self.device
          )
          self.model.emb_rr_distance = self.model.emb_rr_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_coref.py
imports: import sys
  import os
  import torch
  import torch.nn
  import logging
  from transformers import T5Tokenizer
  from .t5_coref import T5Coref
label_desc: Use image node_id 3 for calling the CorefModel obj's underlying member method code with example usage: obj.parallel_preparation_inference() without return types