docs: def fft2(a, s=None, axes=(-2, -1), norm=None):
      """
      Compute the 2-dimensional discrete Fourier Transform
  
      This function computes the *n*-dimensional discrete Fourier Transform
      over any axes in an *M*-dimensional array by means of the
      Fast Fourier Transform (FFT).  By default, the transform is computed over
      the last two axes of the input array, i.e., a 2-dimensional FFT.
  
      Parameters
      ----------
      a : array_like
          Input array, can be complex
      s : sequence of ints, optional
          Shape (length of each transformed axis) of the output
          (`s[0]` refers to axis 0, `s[1]` to axis 1, etc.).
          This corresponds to `n` for `fft(x, n)`.
          Along each axis, if the given shape is smaller than that of the input,
          the input is cropped.  If it is larger, the input is padded with zeros.
          if `s` is not given, the shape of the input along the axes specified
          by `axes` is used.
      axes : sequence of ints, optional
          Axes over which to compute the FFT.  If not given, the last two
          axes are used.  A repeated index in `axes` means the transform over
          that axis is performed multiple times.  A one-element sequence means
          that a one-dimensional FFT is performed.
      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 axes
          indicated by `axes`, or the last two axes if `axes` is not given.
  
      Raises
      ------
      ValueError
          If `s` and `axes` have different length, or `axes` not given and
          ``len(s) != 2``.
      IndexError
          If an element of `axes` is larger than than the number of axes of `a`.
  
      See Also
      --------
      numpy.fft : Overall view of discrete Fourier transforms, with definitions
           and conventions used.
      ifft2 : The inverse two-dimensional FFT.
      fft : The one-dimensional FFT.
      fftn : The *n*-dimensional FFT.
      fftshift : Shifts zero-frequency terms to the center of the array.
          For two-dimensional input, swaps first and third quadrants, and second
          and fourth quadrants.
  
      Notes
      -----
      `fft2` is just `fftn` with a different default for `axes`.
  
      The output, analogously to `fft`, contains the term for zero frequency in
      the low-order corner of the transformed axes, the positive frequency terms
      in the first half of these axes, the term for the Nyquist frequency in the
      middle of the axes and the negative frequency terms in the second half of
      the axes, in order of decreasingly negative frequency.
  
      See `fftn` for details and a plotting example, and `numpy.fft` for
      definitions and conventions used.
  
  
      Examples
      --------
      >>> a = np.mgrid[:5, :5][0]
      >>> np.fft.fft2(a)
      array([[ 50.0 +0.j        ,   0.0 +0.j        ,   0.0 +0.j        ,
                0.0 +0.j        ,   0.0 +0.j        ],
             [-12.5+17.20477401j,   0.0 +0.j        ,   0.0 +0.j        ,
                0.0 +0.j        ,   0.0 +0.j        ],
             [-12.5 +4.0614962j ,   0.0 +0.j        ,   0.0 +0.j        ,
                0.0 +0.j        ,   0.0 +0.j        ],
             [-12.5 -4.0614962j ,   0.0 +0.j        ,   0.0 +0.j        ,
                  0.0 +0.j        ,   0.0 +0.j        ],
             [-12.5-17.20477401j,   0.0 +0.j        ,   0.0 +0.j        ,
                0.0 +0.j        ,   0.0 +0.j        ]])
  
      """
  
      return fftn(a, s=s, axes=axes, norm=norm)