class_docstr: Rescores source and target tokens based on a model
docstr: Initialize a rescorer model
  
  Args:
    args: model arguments
    model_path: checkpoint path for rescoring model
docstr_tok: ["Initialize","a","rescorer","model","Args",":","args",":","model","arguments","model_path",":","checkpoint","path","for","rescoring","model"]
code: def __init__(
      self, args, model_path=None, model=None, forward_task=None
  ):
      """Initialize a rescorer model
  
      Args:
        args: model arguments
        model_path: checkpoint path for rescoring model
      """
      # TODO (T40938917): Allow loading of multiple rescoring models
      # allow to create an empty scorer w/o model
      self.args = args
      self.forward_task = forward_task
      self.task = None
      self.model = None
      # Instantiate the model
      if model is not None:
          self.model = model["model"]
          self.task = model["task"]
      elif model_path:
          (
              rescoring_model,
              _,
              task,
          ) = utils.load_diverse_ensemble_for_inference([model_path])
          self.model = rescoring_model[0]
          self.task = task
  
      if self.model is not None:
          self.model.eval()
          # Turn off gradient computation in eval mode
          for param in self.model.parameters():
              param.requires_grad = False
          utils.maybe_cuda(self.model)
  
imports: import torch
  from pytorch_translate import utils
label_desc: Use image node_id 1 to create a new SimpleModelScorer object from inherited base classes: object with example: obj = SimpleModelScorer(args, model_path, model, forward_task)