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: Combine two feature sets to create a new one.
  
  The combination is done assuming they both have the same instances
  with the same IDs in the same order.
  
  Parameters
  ----------
  other : :class:`skll.data.featureset.FeatureSet`
      The other ``FeatureSet`` to add to this one.
  
  Returns
  -------
  :class:`skll.data.featureset.FeatureSet
      The combined feature set.
  
  Raises
  ------
  ValueError
      If IDs are not in the same order in each ``FeatureSet`` instance.
  
  ValueError
      If either the 'features' or 'vectorizer' attributes are
      ``None`` for either of the two ``FeatureSet`` instances.
  
  ValueError
      If vectorizers are different between the two ``FeatureSet`` instances.
  
  ValueError
      If there are duplicate feature names.
  
  ValueError
      If there are conflicting labels.
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code: def __add__(self, other: "FeatureSet") -> "FeatureSet":
      """
      Combine two feature sets to create a new one.
  
      The combination is done assuming they both have the same instances
      with the same IDs in the same order.
  
      Parameters
      ----------
      other : :class:`skll.data.featureset.FeatureSet`
          The other ``FeatureSet`` to add to this one.
  
      Returns
      -------
      :class:`skll.data.featureset.FeatureSet
          The combined feature set.
  
      Raises
      ------
      ValueError
          If IDs are not in the same order in each ``FeatureSet`` instance.
  
      ValueError
          If either the 'features' or 'vectorizer' attributes are
          ``None`` for either of the two ``FeatureSet`` instances.
  
      ValueError
          If vectorizers are different between the two ``FeatureSet`` instances.
  
      ValueError
          If there are duplicate feature names.
  
      ValueError
          If there are conflicting labels.
      """
      # Check that the sets of IDs are equal
      if set(self.ids) != set(other.ids):
          raise ValueError(
              "IDs are not in the same order in each " "feature set"
          )
      # Compute the relative ordering of IDs for merging the features
      # and labels.
      ids_indices = dict((y, x) for x, y in enumerate(other.ids))
      relative_order = [ids_indices[self_id] for self_id in self.ids]
  
      # Initialize the new feature set with a name and the IDs.
      new_set = FeatureSet(
          "+".join(sorted([self.name, other.name])), deepcopy(self.ids)
      )
  
      # Make sure that features and vectorizer in either feature set are not None
      if (
          self.vectorizer is None
          or other.vectorizer is None
          or self.features is None
          or other.features is None
      ):
          raise ValueError(
              "Cannot combine FeatureSets since either the vectorizer "
              "or the features are not defined."
          )
  
      # Make sure the two vectorizers are the same type
      if not isinstance(self.vectorizer, type(other.vectorizer)):
          raise ValueError(
              "Cannot combine FeatureSets because they are "
              "not both using the same type of feature "
              "vectorizer (e.g., DictVectorizer, "
              "FeatureHasher)"
          )
      # they have to be the same types in this block
      else:
          uses_feature_hasher = isinstance(
              self.vectorizer, FeatureHasher
          )
          if uses_feature_hasher:
              if (
                  self.vectorizer.n_features
                  != other.vectorizer.n_features
              ):
                  raise ValueError(
                      "Cannot combine FeatureSets that use "
                      "FeatureHashers with different values of "
                      "n_features setting."
                  )
          else:
              # Check for duplicate feature names.
              if set(self.vectorizer.feature_names_) & set(
                  other.vectorizer.feature_names_
              ):
                  raise ValueError(
                      "Cannot combine FeatureSets because they have duplicate feature names."
                  )
          num_feats = self.features.shape[1]
  
          new_set.features = sp.hstack(
              [self.features, other.features[relative_order]], "csr"
          )
          new_set.vectorizer = deepcopy(self.vectorizer)
          if not uses_feature_hasher:
              for (
                  feat_name,
                  index,
              ) in other.vectorizer.vocabulary_.items():
                  new_set.vectorizer.vocabulary_[feat_name] = (
                      index + num_feats
                  )
              other_names = other.vectorizer.feature_names_
              new_set.vectorizer.feature_names_.extend(other_names)
  
          # If either set has labels, check that they don't conflict.
          if self.has_labels:
              # labels should be the same for each FeatureSet, so store once.
              conflicts = not np.all(self.labels == other.labels[relative_order])  # type: ignore
              if other.has_labels and conflicts:
                  raise ValueError(
                      "Feature sets have conflicting labels for examples with the same ID."
                  )
              new_set.labels = deepcopy(self.labels)
          else:
              labels = other.labels
              if other.has_labels:
                  labels = deepcopy(other.labels[relative_order])  # type: ignore
              new_set.labels = labels
  
      return new_set
  
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 6 for calling the FeatureSet obj's underlying member method code with example usage: obj.__add__(other) and returns: new_set