# Buggy code
def fft(a, n=None, axis=-1, norm=None):
    """
    Compute the one-dimensional discrete Fourier Transform.

    This function computes the one-dimensional *n*-point discrete Fourier
    Transform (DFT) with the efficient Fast Fourier Transform (FFT)
    algorithm [CT].

    Parameters
    ----------
    a : array_like
        Input array, can be complex.
    n : int, optional
        Length of the transformed axis of the output.
        If `n` is smaller than the length of the input, the input is cropped.
        If it is larger, the input is padded with zeros.  If `n` is not given,
        the length of the input along the axis specified by `axis` is used.
    axis : int, optional
        Axis over which to compute the FFT.  If not given, the last axis is
        used.
    norm : {None, "ortho"}, optional
        .. versionadded:: 1.10.0

        Normalization mode (see `numpy.fft`). Default is None.

    Returns
    -------
    out : complex ndarray
        The truncated or zero-padded input, transformed along the axis
        indicated by `axis`, or the last one if `axis` is not specified.

    Raises
    ------
    IndexError
        if `axes` is larger than the last axis of `a`.

    See Also
    --------
    numpy.fft : for definition of the DFT and conventions used.
    ifft : The inverse of `fft`.
    fft2 : The two-dimensional FFT.
    fftn : The *n*-dimensional FFT.
    rfftn : The *n*-dimensional FFT of real input.
    fftfreq : Frequency bins for given FFT parameters.

    Notes
    -----
    FFT (Fast Fourier Transform) refers to a way the discrete Fourier
    Transform (DFT) can be calculated efficiently, by using symmetries in the
    calculated terms.  The symmetry is highest when `n` is a power of 2, and
    the transform is therefore most efficient for these sizes.

    The DFT is defined, with the conventions used in this implementation, in
    the documentation for the `numpy.fft` module.

    References
    ----------
    .. [CT] Cooley, James W., and John W. Tukey, 1965, "An algorithm for the
            machine calculation of complex Fourier series," *Math. Comput.*
            19: 297-301.

    Examples
    --------
    >>> np.fft.fft(np.exp(2j * np.pi * np.arange(8) / 8))
    array([-2.33486982e-16+1.14423775e-17j,  8.00000000e+00-1.25557246e-15j,
            2.33486982e-16+2.33486982e-16j,  0.00000000e+00+1.22464680e-16j,
           -1.14423775e-17+2.33486982e-16j,  0.00000000e+00+5.20784380e-16j,
            1.14423775e-17+1.14423775e-17j,  0.00000000e+00+1.22464680e-16j])

    In this example, real input has an FFT which is Hermitian, i.e., symmetric
    in the real part and anti-symmetric in the imaginary part, as described in
    the `numpy.fft` documentation:

    >>> import matplotlib.pyplot as plt
    >>> t = np.arange(256)
    >>> sp = np.fft.fft(np.sin(t))
    >>> freq = np.fft.fftfreq(t.shape[-1])
    >>> plt.plot(freq, sp.real, freq, sp.imag)
    [<matplotlib.lines.Line2D object at 0x...>, <matplotlib.lines.Line2D object at 0x...>]
    >>> plt.show()

    """

    a = asarray(a)
    if n is None:
        n = a.shape[axis]
    fct = 1
    if norm is not None and _unitary(norm):
        fct = 1 / sqrt(n)
    output = _raw_fft(a, n, axis, False, True, fct)
    return output


# Traceback (ZeroDivisionError)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<__array_function__ internals>", line 6, in ifft
File "/home/peter/.local/lib/python3.7/site-packages/numpy/fft/pocketfft.py", line 274, in ifft
fct = 1/n
ZeroDivisionError: division by zero

# Fixed code
def fft(a, n=None, axis=-1, norm=None):
    """
    Compute the one-dimensional discrete Fourier Transform.

    This function computes the one-dimensional *n*-point discrete Fourier
    Transform (DFT) with the efficient Fast Fourier Transform (FFT)
    algorithm [CT].

    Parameters
    ----------
    a : array_like
        Input array, can be complex.
    n : int, optional
        Length of the transformed axis of the output.
        If `n` is smaller than the length of the input, the input is cropped.
        If it is larger, the input is padded with zeros.  If `n` is not given,
        the length of the input along the axis specified by `axis` is used.
    axis : int, optional
        Axis over which to compute the FFT.  If not given, the last axis is
        used.
    norm : {None, "ortho"}, optional
        .. versionadded:: 1.10.0

        Normalization mode (see `numpy.fft`). Default is None.

    Returns
    -------
    out : complex ndarray
        The truncated or zero-padded input, transformed along the axis
        indicated by `axis`, or the last one if `axis` is not specified.

    Raises
    ------
    IndexError
        if `axes` is larger than the last axis of `a`.

    See Also
    --------
    numpy.fft : for definition of the DFT and conventions used.
    ifft : The inverse of `fft`.
    fft2 : The two-dimensional FFT.
    fftn : The *n*-dimensional FFT.
    rfftn : The *n*-dimensional FFT of real input.
    fftfreq : Frequency bins for given FFT parameters.

    Notes
    -----
    FFT (Fast Fourier Transform) refers to a way the discrete Fourier
    Transform (DFT) can be calculated efficiently, by using symmetries in the
    calculated terms.  The symmetry is highest when `n` is a power of 2, and
    the transform is therefore most efficient for these sizes.

    The DFT is defined, with the conventions used in this implementation, in
    the documentation for the `numpy.fft` module.

    References
    ----------
    .. [CT] Cooley, James W., and John W. Tukey, 1965, "An algorithm for the
            machine calculation of complex Fourier series," *Math. Comput.*
            19: 297-301.

    Examples
    --------
    >>> np.fft.fft(np.exp(2j * np.pi * np.arange(8) / 8))
    array([-2.33486982e-16+1.14423775e-17j,  8.00000000e+00-1.25557246e-15j,
            2.33486982e-16+2.33486982e-16j,  0.00000000e+00+1.22464680e-16j,
           -1.14423775e-17+2.33486982e-16j,  0.00000000e+00+5.20784380e-16j,
            1.14423775e-17+1.14423775e-17j,  0.00000000e+00+1.22464680e-16j])

    In this example, real input has an FFT which is Hermitian, i.e., symmetric
    in the real part and anti-symmetric in the imaginary part, as described in
    the `numpy.fft` documentation:

    >>> import matplotlib.pyplot as plt
    >>> t = np.arange(256)
    >>> sp = np.fft.fft(np.sin(t))
    >>> freq = np.fft.fftfreq(t.shape[-1])
    >>> plt.plot(freq, sp.real, freq, sp.imag)
    [<matplotlib.lines.Line2D object at 0x...>, <matplotlib.lines.Line2D object at 0x...>]
    >>> plt.show()

    """

    a = asarray(a)
    if n is None:
        n = a.shape[axis]
    inv_norm = 1
    if norm is not None and _unitary(norm):
        inv_norm = sqrt(n)
    output = _raw_fft(a, n, axis, False, True, inv_norm)
    return output