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: Forward and inverse transform.
  
  Args:
    x : ndarray of shape (n_samples, n_features)
      The data used to scale along the features axis.
    inverse : bool, default=False
      If False, apply forward transform. If True, apply
      inverse transform.
  
  Returns:
    x : ndarray of shape (n_samples, n_features)
      Projected data
docstr_tok: ["Forward","and","inverse","transform",".","Args",":","x",":","ndarray","of","shape","(","n_samples",",","n_features",")","The","data","used","to","scale","along","the","features","axis",".","inverse",":","bool",",","default=False","If","False",",","apply","forward","transform",".","If","True",",","apply","inverse","transform",".","Returns",":","x",":","ndarray","of","shape","(","n_samples",",","n_features",")","Projected","data"]
code: def _transform(self, x, inverse=False):
      """Forward and inverse transform.
  
      Args:
        x : ndarray of shape (n_samples, n_features)
          The data used to scale along the features axis.
        inverse : bool, default=False
          If False, apply forward transform. If True, apply
          inverse transform.
  
      Returns:
        x : ndarray of shape (n_samples, n_features)
          Projected data
      """
      x = np.array(x)  # Explicit copy.
      for feature_idx in range(x.shape[1]):
          x[:, feature_idx] = self._transform_col(
              x[:, feature_idx],
              self.quantiles_[:, feature_idx],
              inverse,
          )
      return x
  
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 5 for calling the QuantileTransformer obj's underlying member method code with example usage: obj._transform(x, inverse) and returns: x