class_docstr: Encapsulate features, labels, and metadata for a given dataset.
  
  Parameters
  ----------
  name : str
      The name of this feature set.
  
  ids : Union[List[str], numpy.ndarray]
      Example IDs for this set.
  
  labels : Optional[Union[List[str], numpy.ndarray], default=None
      Labels for this set.
  
  features : Optional[Union[:class:`skll.types.FeatureDictList`, :class:`numpy.ndarray`]], default=None
      The features for each instance represented as either a
      list of dictionaries or a numpy array (if ``vectorizer`` is
      also specified).
  
  vectorizer : Optional[Union[:class:`sklearn.feature_extraction.DictVectorizer`, :class:`sklearn.feature_extraction.FeatureHasher`], default=None
      Vectorizer which will be used to generate the feature matrix.
  
  Warnings
  --------
  FeatureSets can only be equal if the order of the instances is
  identical because these are stored as lists/arrays. Since scikit-learn's
  ``DictVectorizer`` automatically sorts the underlying feature matrix
  if it is sparse, we do not do any sorting before checking for equality.
  This is not a problem because we _always_ use sparse matrices with
  ``DictVectorizer`` when creating FeatureSets.
  
  Notes
  -----
  If ids, labels, and/or features are not None, the number of rows in
  each array must be equal.
docstr: Get new feature subset or specific example.
  
  Parameters
  ----------
  value: Union[int, slice]
      The value to use for retrieval. This can either be a slice or
      an index.
  
  Returns
  -------
  Union[:class:`skll.data.featureset.FeatureSet`, Tuple[:class:`skll.types.IdType`, :class:`skll.types.LabelType`, :class:`skll.types.FeatureDictList`]]  # noqa: E501
      If `value` is a slice, then return a new ``FeatureSet`` instance
      containing a subset of the data. If it's an index, return the
      specific example by row number.
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code: def __getitem__(
      self, value: Union[int, slice]
  ) -> Union["FeatureSet", Tuple[IdType, LabelType, FeatureDictList]]:
      """
      Get new feature subset or specific example.
  
      Parameters
      ----------
      value: Union[int, slice]
          The value to use for retrieval. This can either be a slice or
          an index.
  
      Returns
      -------
      Union[:class:`skll.data.featureset.FeatureSet`, Tuple[:class:`skll.types.IdType`, :class:`skll.types.LabelType`, :class:`skll.types.FeatureDictList`]]  # noqa: E501
          If `value` is a slice, then return a new ``FeatureSet`` instance
          containing a subset of the data. If it's an index, return the
          specific example by row number.
      """
      # Check if we're slicing
      if isinstance(value, slice):
          sliced_ids = self.ids[value]
          sliced_feats = (
              self.features[value]
              if self.features is not None
              else None
          )
          sliced_labels = (
              self.labels[value] if self.labels is not None else None
          )
          return FeatureSet(
              f"{self.name}_{value}",
              sliced_ids,
              features=sliced_feats,
              labels=sliced_labels,
              vectorizer=self.vectorizer,
          )
      else:
          label = self.labels[value] if self.labels is not None else ""
          if self.features is not None and self.vectorizer:
              submatrix = self.features[value, :]
              features = self.vectorizer.inverse_transform(submatrix)[0]
          else:
              features = [{}]
          return self.ids[value], label, features
  
imports: from copy import deepcopy
  from typing import Collection, List, Optional, Tuple, Union
  import numpy
  import scipy.sparse
  from pandas import DataFrame
  from sklearn.feature_extraction import DictVectorizer, FeatureHasher
  from skll.data.dict_vectorizer import DictVectorizer
  from skll.types import FeatGenerator, FeatureDictList, IdType, LabelType, SparseFeatureMatrix
label_desc: Use image node_id 13 for calling the FeatureSet obj's underlying member method code with example usage: obj.__getitem__(value) and returns: FeatureSet, self,  label,  features