class_docstr: The SST2 dataset.
  
  This dataset is constructed using the Stanford Sentiment Treebank Dataset.
  This dataset contains binary labels (positive or negative) for each sample.
  
  The original dataset specified 5 labels:
  very negative, negative, neutral, positive, very positive with
  the following cutoffs:
  [0, 0.2], (0.2, 0.4], (0.4, 0.6], (0.6, 0.8], (0.8, 1.0]
  
  In the construction of this dataset, we remove all neutral phrases
  and assign a negative label if the original rating falls
  into the following range: [0, 0.4] and a positive label
  if the original rating is between (0.6, 1.0].
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code: def __init__(
      self,
      config: DatasetConfig,
      cache_dir: Optional[str] = None,
      include_subtrees=False,
      convert_parentheses=True,
      remove_duplicates=False,
  ):
      super().__init__(
          config,
          cache_dir=cache_dir,
          include_subtrees=include_subtrees,
          discard_neutral=True,
          convert_parentheses=convert_parentheses,
          remove_duplicates=remove_duplicates,
      )
  
imports: import os
  from typing import List, Optional, Set
  import pandas
  from ludwig.datasets.dataset_config import DatasetConfig
  from ludwig.datasets.loaders.dataset_loader import DatasetLoader
label_desc: Use image node_id 1 to create a new SST2Loader object from inherited base classes: SSTLoader with example: obj = SST2Loader(config, cache_dir, include_subtrees, convert_parentheses, remove_duplicates)