class_docstr: Transforms lists of feature-value mappings to vectors.
  
  This transformer turns lists of mappings (dict-like objects) of feature
  names to feature values into Numpy arrays or scipy.sparse matrices for use
  with scikit-learn estimators.
  
  When feature values are strings, this transformer will do a binary one-hot
  (aka one-of-K) coding: one boolean-valued feature is constructed for each
  of the possible string values that the feature can take on. For instance,
  a feature "f" that can take on the values "ham" and "spam" will become two
  features in the output, one signifying "f=ham", the other "f=spam".
  
  If a feature value is a sequence or set of strings, this transformer
  will iterate over the values and will count the occurrences of each string
  value.
  
  However, note that this transformer will only do a binary one-hot encoding
  when feature values are of type string. If categorical features are
  represented as numeric values such as int or iterables of strings, the
  DictVectorizer can be followed by
  :class:`~sklearn.preprocessing.OneHotEncoder` to complete
  binary one-hot encoding.
  
  Features that do not occur in a sample (mapping) will have a zero value
  in the resulting array/matrix.
  
  For an efficiency comparision of the different feature extractors, see
  :ref:`sphx_glr_auto_examples_text_plot_hashing_vs_dict_vectorizer.py`.
  
  Read more in the :ref:`User Guide <dict_feature_extraction>`.
  
  Parameters
  ----------
  dtype : dtype, default=np.float64
      The type of feature values. Passed to Numpy array/scipy.sparse matrix
      constructors as the dtype argument.
  separator : str, default="="
      Separator string used when constructing new features for one-hot
      coding.
  sparse : bool, default=True
      Whether transform should produce scipy.sparse matrices.
  sort : bool, default=True
      Whether ``feature_names_`` and ``vocabulary_`` should be
      sorted when fitting.
  
  Attributes
  ----------
  vocabulary_ : dict
      A dictionary mapping feature names to feature indices.
  
  feature_names_ : list
      A list of length n_features containing the feature names (e.g., "f=ham"
      and "f=spam").
  
  See Also
  --------
  FeatureHasher : Performs vectorization using only a hash function.
  sklearn.preprocessing.OrdinalEncoder : Handles nominal/categorical
      features encoded as columns of arbitrary data types.
  
  Examples
  --------
  >>> from sklearn.feature_extraction import DictVectorizer
  >>> v = DictVectorizer(sparse=False)
  >>> D = [{'foo': 1, 'bar': 2}, {'foo': 3, 'baz': 1}]
  >>> X = v.fit_transform(D)
  >>> X
  array([[2., 0., 1.],
         [0., 1., 3.]])
  >>> v.inverse_transform(X) == [{'bar': 2.0, 'foo': 1.0},
  ...                            {'baz': 1.0, 'foo': 3.0}]
  True
  >>> v.transform({'foo': 4, 'unseen_feature': 3})
  array([[0., 0., 4.]])
docstr: Get output feature names for transformation.
  
  Parameters
  ----------
  input_features : array-like of str or None, default=None
      Not used, present here for API consistency by convention.
  
  Returns
  -------
  feature_names_out : ndarray of str objects
      Transformed feature names.
docstr_tok: ["Get","output","feature","names","for","transformation",".","Parameters","--","--","--","--","--","input_features",":","array-like","of","str","or","None",",","default=None","Not","used",",","present","here","for","API","consistency","by","convention",".","Returns","--","--","--","-","feature_names_out",":","ndarray","of","str","objects","Transformed","feature","names","."]
code: def get_feature_names_out(self, input_features=None):
      """Get output feature names for transformation.
  
      Parameters
      ----------
      input_features : array-like of str or None, default=None
          Not used, present here for API consistency by convention.
  
      Returns
      -------
      feature_names_out : ndarray of str objects
          Transformed feature names.
      """
      check_is_fitted(self, "feature_names_")
      if any(not isinstance(name, str) for name in self.feature_names_):
          feature_names = [str(name) for name in self.feature_names_]
      else:
          feature_names = self.feature_names_
      return np.asarray(feature_names, dtype=object)
  
imports: from array import array
  from collections.abc import Iterable, Mapping
  from numbers import Number
  from operator import itemgetter
  import numpy
  import scipy.sparse
  from ..base import BaseEstimator, TransformerMixin, _fit_context
  from ..utils import check_array
  from ..utils.validation import check_is_fitted
label_desc: Use image node_id 8 for calling the DictVectorizer obj's underlying member method code with example usage: obj.get_feature_names_out(input_features) and returns: np