class_docstr: The SST5 dataset.
  
  This dataset is constructed using the Stanford Sentiment Treebank Dataset.
  This dataset contains five labels (very negative, negative, neutral,
  positive, very positive) for each sample.
  
  In the original dataset, the  5 labels: very negative, negative, neutral, positive,
  and very positive have the following cutoffs:
  [0, 0.2], (0.2, 0.4], (0.4, 0.6], (0.6, 0.8], (0.8, 1.0]
  
  This class pulls in an array of mixins for different types of functionality
  which belongs in the workflow for ingesting and transforming
  training data into a destination dataframe that can be use by Ludwig.
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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,
          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 SST5Loader object from inherited base classes: SSTLoader with example: obj = SST5Loader(config, cache_dir, include_subtrees, convert_parentheses, remove_duplicates)