code: def __init__(
      self, data_file: str, batch_size: int, vectorizer: Vectorizer
  ):
      self.df = pd.read_csv(data_file)
  
      # preprocessing
      self.vectorizer: Vectorizer = vectorizer
  
      self.df["x_in"] = self.df.apply(
          lambda row: self.vectorizer.vectorize(row.title), axis=1
      )
      self.df["y_target"] = self.df.apply(
          lambda row: self.vectorizer.target_vocab.lookup_token(
              row.category
          ),
          axis=1,
      )
  
      train_df = self.df[self.df.split == "train"][["x_in", "y_target"]]
      val_df = self.df[self.df.split == "val"][["x_in", "y_target"]]
      test_df = self.df[self.df.split == "test"][["x_in", "y_target"]]
  
      super().__init__(
          train_set=DataFrameDataset(train_df),
          train_batch_size=batch_size,
          val_set=DataFrameDataset(val_df),
          val_batch_size=batch_size,
          test_set=DataFrameDataset(test_df),
          test_batch_size=batch_size,
      )
  
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 NewsDataset object from inherited base classes: DatasetSplits with example: obj = NewsDataset(data_file, batch_size, vectorizer)