class_docstr: Calculate score of triplet (h, r, t) based on pretained KG embeddings
      using specified score_function
  Parameters
  ---------
  device : int
      Device to run the inference, -1 for CPU
  config : dict
      Containing KG model information
  model_path : str
      path storing the model (pretrained embeddings)
  score_func : str
      What kind of score is used,
          none: score = $x$
          logsigmoid: score $log(sigmoid(x))
class_docstr_tok: ["Calculate","score","of","triplet","(","h",",","r",",","t",")","based","on","pretained","KG","embeddings","using","specified","score_function","Parameters","--","--","--","--","-","device",":","int","Device","to","run","the","inference",",","-1","for","CPU","config",":","dict","Containing","KG","model","information","model_path",":","str","path","storing","the","model","(","pretrained","embeddings",")","score_func",":","str","What","kind","of","score","is","used",",","none",":","score","=","$","x","$","logsigmoid",":","score","$","log","(","sigmoid","(","x",")",")"]
code: def __init__(self, device, config, model_path, sfunc="none"):
      assert sfunc in [
          "none",
          "logsigmoid",
      ], "score function should be none or logsigmoid"
  
      self.device = "cpu" if device < 0 else device
      self.config = config
      self.model_path = model_path
      self.sfunc = sfunc
      if sfunc == "none":
          self.score_func = none
      else:
          self.score_func = logsigmoid
  
file_path: ogb/examples/lsc/wikikg90m/dgl-ke-ogb-lsc/python/dglke/models/infer.py
imports: import os
  import time
  import argparse
  import numpy
  import dgl.backend
  from .general_models import InferModel
label_desc: Use image node_id 1 to create a new ScoreInfer object from inherited base classes: object with example: obj = ScoreInfer(device, config, model_path, sfunc)