class_docstr: Implements feature hashing, aka the hashing trick.
  
  This class turns sequences of symbolic feature names (strings) into
  scipy.sparse matrices, using a hash function to compute the matrix column
  corresponding to a name. The hash function employed is the signed 32-bit
  version of Murmurhash3.
  
  Feature names of type byte string are used as-is. Unicode strings are
  converted to UTF-8 first, but no Unicode normalization is done.
  Feature values must be (finite) numbers.
  
  This class is a low-memory alternative to DictVectorizer and
  CountVectorizer, intended for large-scale (online) learning and situations
  where memory is tight, e.g. when running prediction code on embedded
  devices.
  
  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 <feature_hashing>`.
  
  .. versionadded:: 0.13
  
  Parameters
  ----------
  n_features : int, default=2**20
      The number of features (columns) in the output matrices. Small numbers
      of features are likely to cause hash collisions, but large numbers
      will cause larger coefficient dimensions in linear learners.
  input_type : str, default='dict'
      Choose a string from {'dict', 'pair', 'string'}.
      Either "dict" (the default) to accept dictionaries over
      (feature_name, value); "pair" to accept pairs of (feature_name, value);
      or "string" to accept single strings.
      feature_name should be a string, while value should be a number.
      In the case of "string", a value of 1 is implied.
      The feature_name is hashed to find the appropriate column for the
      feature. The value's sign might be flipped in the output (but see
      non_negative, below).
  dtype : numpy dtype, default=np.float64
      The type of feature values. Passed to scipy.sparse matrix constructors
      as the dtype argument. Do not set this to bool, np.boolean or any
      unsigned integer type.
  alternate_sign : bool, default=True
      When True, an alternating sign is added to the features as to
      approximately conserve the inner product in the hashed space even for
      small n_features. This approach is similar to sparse random projection.
  
      .. versionchanged:: 0.19
          ``alternate_sign`` replaces the now deprecated ``non_negative``
          parameter.
  
  See Also
  --------
  DictVectorizer : Vectorizes string-valued features using a hash table.
  sklearn.preprocessing.OneHotEncoder : Handles nominal/categorical features.
  
  Notes
  -----
  This estimator is :term:`stateless` and does not need to be fitted.
  However, we recommend to call :meth:`fit_transform` instead of
  :meth:`transform`, as parameter validation is only performed in
  :meth:`fit`.
  
  Examples
  --------
  >>> from sklearn.feature_extraction import FeatureHasher
  >>> h = FeatureHasher(n_features=10)
  >>> D = [{'dog': 1, 'cat':2, 'elephant':4},{'dog': 2, 'run': 5}]
  >>> f = h.transform(D)
  >>> f.toarray()
  array([[ 0.,  0., -4., -1.,  0.,  0.,  0.,  0.,  0.,  2.],
         [ 0.,  0.,  0., -2., -5.,  0.,  0.,  0.,  0.,  0.]])
  
  With `input_type="string"`, the input must be an iterable over iterables of
  strings:
  
  >>> h = FeatureHasher(n_features=8, input_type="string")
  >>> raw_X = [["dog", "cat", "snake"], ["snake", "dog"], ["cat", "bird"]]
  >>> f = h.transform(raw_X)
  >>> f.toarray()
  array([[ 0.,  0.,  0., -1.,  0., -1.,  0.,  1.],
         [ 0.,  0.,  0., -1.,  0., -1.,  0.,  0.],
         [ 0., -1.,  0.,  0.,  0.,  0.,  0.,  1.]])
docstr: Only validates estimator's parameters.
  
  This method allows to: (i) validate the estimator's parameters and
  (ii) be consistent with the scikit-learn transformer API.
  
  Parameters
  ----------
  X : Ignored
      Not used, present here for API consistency by convention.
  
  y : Ignored
      Not used, present here for API consistency by convention.
  
  Returns
  -------
  self : object
      FeatureHasher class instance.
docstr_tok: ["Only","validates","estimator","'s","parameters",".","This","method","allows","to",":","(","i",")","validate","the","estimator","'s","parameters","and","(","ii",")","be","consistent","with","the","scikit-learn","transformer","API",".","Parameters","--","--","--","--","--","X",":","Ignored","Not","used",",","present","here","for","API","consistency","by","convention",".","y",":","Ignored","Not","used",",","present","here","for","API","consistency","by","convention",".","Returns","--","--","--","-","self",":","object","FeatureHasher","class","instance","."]
code: def fit(self, X=None, y=None):
      """Only validates estimator's parameters.
  
      This method allows to: (i) validate the estimator's parameters and
      (ii) be consistent with the scikit-learn transformer API.
  
      Parameters
      ----------
      X : Ignored
          Not used, present here for API consistency by convention.
  
      y : Ignored
          Not used, present here for API consistency by convention.
  
      Returns
      -------
      self : object
          FeatureHasher class instance.
      """
      return self
  
file_path: catboost/contrib/python/scikit-learn/py3/sklearn/feature_extraction/_hash.py
imports: from itertools import chain
  from numbers import Integral
  import numpy
  import scipy.sparse
  from ..base import BaseEstimator, TransformerMixin, _fit_context
  from ..utils._param_validation import Interval, StrOptions
  from ._hashing_fast import transform
label_desc: Use image node_id 2 for calling the FeatureHasher obj's underlying member method code with example usage: obj.fit(X, y) and returns: self