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 json_to_data(self, input_json: Dict) -> Dict:
      return {
          "x_in": torch.LongTensor(
              [
                  self.vectorizer.vectorize(title=input_string)
                  for input_string in input_json["inputs"]
              ]
          )
      }
  
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 2 for calling the NewsPredictor obj's underlying member method code with example usage: obj.json_to_data(input_json) and returns: dict