code: def __init__(self, vectorizer: Vectorizer, model: torch.nn.Module):
      self.model: torch.nn.Module = model
      self.model.eval()
      self.forward_params = {}
      model_spec = inspect.getfullargspec(self.model.forward)
      for fparam, pdefault in zip_longest(
          reversed(model_spec.args[1:]),
          reversed(model_spec.defaults if model_spec.defaults else []),
      ):
          self.forward_params[fparam] = pdefault
  
      self.vectorizer: Vectorizer = vectorizer
  
file_path: transfer-nlp/transfer_nlp/plugins/predictors.py
imports: import inspect
  import logging
  from itertools import zip_longest
  from typing import Dict, List, Any
  import torch
  from ignite.utils import convert_tensor
  from transfer_nlp.loaders.vectorizers import Vectorizer
label_desc: Use image node_id 1 to create a new PredictorABC object  with example: obj = PredictorABC(vectorizer, model)