class_docstr: Simple Fourier transform for time series.
  
  The implementation is based on the real fast fourier transform from numpy.fft.rfft
  Returns pd.Series of amplitudes of integer range frequencies.
  Even-Sampling of data is assumed and frequency range converted to integer.
  
  Examples
  --------
  >>> from sktime.transformations.series.fourier import FourierTransform
  >>> from sktime.datasets import load_airline
  >>> X = load_airline()
  >>> transformer = FourierTransform()
  >>> X_ft = transformer.fit_transform(X)
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docstr: Transform X and return a transformed version.
  
  private _transform containing core logic, called from transform
  
  Parameters
  ----------
  X : Series mtype X_inner_mtype
  
  Returns
  -------
  transformed version of X
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code: def _transform(self, X, y=None):
      """Transform X and return a transformed version.
  
      private _transform containing core logic, called from transform
  
      Parameters
      ----------
      X : Series mtype X_inner_mtype
  
      Returns
      -------
      transformed version of X
      """
      # numpy.fft methods
      dft_seq = np.abs(rfft(X))
  
      # Combining the arrays to Pandas Series
      Y = pd.Series(dft_seq[1:])
      return Y
  
file_path: sktime/sktime/transformations/series/fourier.py
imports: import warnings
  from typing import List, Optional
  import numpy
  import pandas
  from numpy.fft import rfft
  from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 2 for calling the FourierTransform obj's underlying member method code with example usage: obj._transform(X, y) and returns: Y