class_docstr: Transform samples using quantiles information.
  
  This method transforms the samples to follow a uniform or a normal
  distribution. Therefore, for a given sample, this transformation tends
  to spread out the most frequent values. It also reduces the impact of
  (marginal) outliers: this is therefore a robust preprocessing scheme.
  The transformation is applied on each sample independently.
  
  The cumulative distribution function of a feature is used to project the
  original values. Note that this transform is non-linear.
  
  Parameters
  ----------
  n_quantiles : int, optional (default = 1000)
      Number of quantiles to be computed. It corresponds to the number
      of landmarks used to discretize the cumulative distribution function.
  
  output_distribution : 'uniform' or 'normal' (default = 'uniform')
      Marginal distribution for the transformed data. The choices are
      'uniform' (default) or 'normal'.
  
  subsample : int, optional (default = 1e5)
      Maximum number of timestamps used to estimate the quantiles for
      computational efficiency.
  
  random_state : int, RandomState instance or None, optional (default=None)
      If int, random_state is the seed used by the random number generator;
      If RandomState instance, random_state is the random number generator;
      If None, the random number generator is the RandomState instance used
      by np.random. Note that this is used by subsampling and smoothing
      noise.
  
  Examples
  --------
  >>> from pyts.datasets import load_gunpoint
  >>> from pyts.preprocessing import QuantileTransformer
  >>> X, _, _, _  = load_gunpoint(return_X_y=True)
  >>> qt = QuantileTransformer(n_quantiles=10)
  >>> qt.transform(X)
  array([...])
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docstr: Pass.
  
  Parameters
  ----------
  X
      Ignored
  
  y
      Ignored
  
  Returns
  -------
  self : object
docstr_tok: ["Pass",".","Parameters","--","--","--","--","--","X","Ignored","y","Ignored","Returns","--","--","--","-","self",":","object"]
code: def fit(self, X=None, y=None):
      """Pass.
  
      Parameters
      ----------
      X
          Ignored
  
      y
          Ignored
  
      Returns
      -------
      self : object
  
      """
      return self
  
file_path: pyts/pyts/preprocessing/transformer.py
imports: from sklearn.base import BaseEstimator
  from sklearn.preprocessing import PowerTransformer
  from sklearn.preprocessing import QuantileTransformer
  from sklearn.utils.validation import check_array
  from ..base import UnivariateTransformerMixin
label_desc: Use image node_id 2 for calling the QuantileTransformer obj's underlying member method code with example usage: obj.fit(X, y) and returns: self