class_docstr: Transform features using quantiles information.
  
  Stripped down version of sklearn.preprocessing.QuantileTransformer.
  https://github.com/scikit-learn/scikit-learn/blob/
  863e58fcd5ce960b4af60362b44d4f33f08c0f97/sklearn/preprocessing/_data.py
  
  Putting directly in ddsp library to avoid dependency on sklearn that breaks
  when pickling and unpickling from different versions of sklearn.
class_docstr_tok: ["Transform","features","using","quantiles","information",".","Stripped","down","version","of","sklearn.preprocessing.QuantileTransformer",".","https",":","\/\/github.com\/scikit-learn\/scikit-learn\/blob\/","863e58fcd5ce960b4af60362b44d4f33f08c0f97\/sklearn\/preprocessing\/_data.py","Putting","directly","in","ddsp","library","to","avoid","dependency","on","sklearn","that","breaks","when","pickling","and","unpickling","from","different","versions","of","sklearn","."]
docstr: Constructor.
  
  Args:
    n_quantiles: int, default=1000 or n_samples Number of quantiles to be
      computed. It corresponds to the number of landmarks used to discretize
      the cumulative distribution function. If n_quantiles is larger than the
      number of samples, n_quantiles is set to the number of samples as a
      larger number of quantiles does not give a better approximation of the
      cumulative distribution function estimator.
    output_distribution: {'uniform', 'normal'}, default='uniform' Marginal
      distribution for the transformed data. The choices are 'uniform'
      (default) or 'normal'.
    subsample: int, default=1e5 Maximum number of samples used to estimate
      the quantiles for computational efficiency. Note that the subsampling
      procedure may differ for value-identical sparse and dense matrices.
docstr_tok: ["Constructor",".","Args",":","n_quantiles",":","int",",","default=1000","or","n_samples","Number","of","quantiles","to","be","computed",".","It","corresponds","to","the","number","of","landmarks","used","to","discretize","the","cumulative","distribution","function",".","If","n_quantiles","is","larger","than","the","number","of","samples",",","n_quantiles","is","set","to","the","number","of","samples","as","a","larger","number","of","quantiles","does","not","give","a","better","approximation","of","the","cumulative","distribution","function","estimator",".","output_distribution",":","{","'uniform","'",",","'normal","'","}",",","default='uniform","'","Marginal","distribution","for","the","transformed","data",".","The","choices","are","'uniform'","(","default",")","or","'normal","'",".","subsample",":","int",",","default=1e5","Maximum","number","of","samples","used","to","estimate","the","quantiles","for","computational","efficiency",".","Note","that","the","subsampling","procedure","may","differ","for","value-identical","sparse","and","dense","matrices","."]
code: def __init__(
      self,
      n_quantiles=1000,
      output_distribution="uniform",
      subsample=int(1e5),
  ):
      """Constructor.
  
      Args:
        n_quantiles: int, default=1000 or n_samples Number of quantiles to be
          computed. It corresponds to the number of landmarks used to discretize
          the cumulative distribution function. If n_quantiles is larger than the
          number of samples, n_quantiles is set to the number of samples as a
          larger number of quantiles does not give a better approximation of the
          cumulative distribution function estimator.
        output_distribution: {'uniform', 'normal'}, default='uniform' Marginal
          distribution for the transformed data. The choices are 'uniform'
          (default) or 'normal'.
        subsample: int, default=1e5 Maximum number of samples used to estimate
          the quantiles for computational efficiency. Note that the subsampling
          procedure may differ for value-identical sparse and dense matrices.
      """
      self.n_quantiles = n_quantiles
      self.output_distribution = output_distribution
      self.subsample = subsample
      self.random_state = np.random.mtrand._rand
  
file_path: ddsp/ddsp/training/postprocessing.py
imports: from ddsp import spectral_ops
  from ddsp.core import hz_to_midi
  import numpy
  from scipy import stats
  import tensorflow.compat.v2
label_desc: Use image node_id 1 to create a new QuantileTransformer object  with example: obj = QuantileTransformer(n_quantiles, output_distribution, subsample)