class_docstr: Toy example: we want to make predictions on inputs of the form {"inputs": ["hello world", "foo", "bar"]}
class_docstr_tok: ["Toy","example",":","we","want","to","make","predictions","on","inputs","of","the","form","{","``","inputs","''",":","[","``","hello","world","''",",","``","foo","''",",","``","bar","''","]","}"]
code: def __init__(self, data: DatasetSplits, model: torch.nn.Module):
      super().__init__(vectorizer=data.vectorizer, model=model)
  
imports: import logging
  import string
  from collections import Counter
  from typing import Dict, List, Any
  import numpy
  import pandas
  import torch
  from transfer_nlp.common.tokenizers import CustomTokenizer
  from transfer_nlp.embeddings.embeddings import Embedding
  from transfer_nlp.loaders.loaders import DatasetSplits, DataFrameDataset
  from transfer_nlp.loaders.vectorizers import Vectorizer
  from transfer_nlp.loaders.vocabulary import Vocabulary, SequenceVocabulary
  from transfer_nlp.plugins.config import register_plugin
  from transfer_nlp.plugins.predictors import PredictorABC
label_desc: Use image node_id 1 to create a new NewsPredictor object from inherited base classes: PredictorABC with example: obj = NewsPredictor(data, model)