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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def two_point_correlation_fft(im):
 r""" Calculates the two-point correlation function using fourier transforms Parameters im : ND-array The image of the void space on which the 2-point correlation is desired Returns ------- result : named_tuple A tuple containing the x and y data for plotting the two-point correlation function, using the *args feature of matplotlib's plot function. The x array is the distances between points and the y array is corresponding probabilities that points of a given distance both lie in the void space. Notes ----- The fourier transform approach utilizes the fact that the autocorrelation function is the inverse FT of the power spectrum density. For background read the Scipy fftpack docs and for a good explanation see: http://www.ucl.ac.uk/~ucapikr/projects/KamilaSuankulova_BSc_Project.pdf """

    # Calculate half lengths of the image
    hls = (np.ceil(np.shape(im))/2).astype(int)
    # Fourier Transform and shift image
    F = sp_ft.ifftshift(sp_ft.fftn(sp_ft.fftshift(im)))
    # Compute Power Spectrum
    P = sp.absolute(F**2)
    # Auto-correlation is inverse of Power Spectrum
    autoc = sp.absolute(sp_ft.ifftshift(sp_ft.ifftn(sp_ft.fftshift(P))))
    tpcf = _radial_profile(autoc, r_max=np.min(hls))
    return tpcf