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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 fit(self, conver=DEFAULT_CONVERGENCE, minit=DEFAULT_MINIT, maxit=DEFAULT_MAXIT, fflag=DEFAULT_FFLAG, maxgerr=DEFAULT_MAXGERR, going_inwards=False):
 """ Fit an elliptical isophote. Parameters conver : float, optional The main convergence criterion. Iterations stop when the largest harmonic amplitude becomes smaller (in absolute value) than ``conver`` times the harmonic fit rms. The default is 0.05. minit : int, optional The minimum number of iterations to perform. A minimum of 10 (the default) iterations guarantees that, on average, 2 iterations will be available for fitting each independent parameter (the four harmonic amplitudes and the intensity level). For the first isophote, the minimum number of iterations is 2 * ``minit`` to ensure that, even departing from not-so-good initial values, the algorithm has a better chance to converge to a sensible solution. maxit : int, optional The maximum number of iterations to perform. The default is 50. fflag : float, optional The acceptable fraction of flagged data points in the sample. If the actual fraction of valid data points is smaller than this, the iterations will stop and the current `~photutils.isophote.Isophote` will be returned. Flagged data points are points that either lie outside the image frame, are masked, or were rejected by sigma-clipping. The default is 0.7. maxgerr : float, optional The maximum acceptable relative error in the local radial intensity gradient. This is the main control for preventing ellipses to grow to regions of too low signal-to-noise ratio. It specifies the maximum acceptable relative error in the local radial intensity gradient. `Busko (1996; ASPC 101, 139) <http://adsabs.harvard.edu/abs/1996ASPC..101..139B>`_ showed that the fitting precision relates to that relative error. The usual behavior of the gradient relative error is to increase with semimajor axis, being larger in outer, fainter regions of a galaxy image. In the current implementation, the ``maxgerr`` criterion is triggered only when two consecutive isophotes exceed the value specified by the parameter. This prevents premature stopping caused by contamination such as stars and HII regions. A number of actions may happen when the gradient error exceeds ``maxgerr`` (or becomes non-significant and is set to `None`). If the maximum semimajor axis specified by ``maxsma`` is set to `None`, semimajor axis growth is stopped and the algorithm proceeds inwards to the galaxy center. If ``maxsma`` is set to some finite value, and this value is larger than the current semimajor axis length, the algorithm enters non-iterative mode and proceeds outwards until reaching ``maxsma``. The default is 0.5. going_inwards : bool, optional Parameter to define the sense of SMA growth. When fitting just one isophote, this parameter is used only by the code that defines the details of how elliptical arc segments ("sectors") are extracted from the image, when using area extraction modes (see the ``integrmode`` parameter in the `~photutils.isophote.EllipseSample` class). The default is `False`. Returns ------- result : `~photutils.isophote.Isophote` instance The fitted isophote, which also contains fit status information. Examples -------- """


        sample = self._sample

        # this flag signals that limiting gradient error (`maxgerr`)
        # wasn't exceeded yet.
        lexceed = False

        # here we keep track of the sample that caused the minimum harmonic
        # amplitude(in absolute value). This will eventually be used to
        # build the resulting Isophote in cases where iterations run to
        # the maximum allowed (maxit), or the maximum number of flagged
        # data points (fflag) is reached.
        minimum_amplitude_value = np.Inf
        minimum_amplitude_sample = None

        for iter in range(maxit):
            # Force the sample to compute its gradient and associated values.
            sample.update()

            # The extract() method returns sampled values as a 2-d numpy array
            # with the following structure:
            # values[0] = 1-d array with angles
            # values[1] = 1-d array with radii
            # values[2] = 1-d array with intensity
            values = sample.extract()

            # Fit harmonic coefficients. Failure in fitting is
            # a fatal error; terminate immediately with sample
            # marked as invalid.
            try:
                coeffs = fit_first_and_second_harmonics(values[0], values[2])
            except Exception as e:
                log.info(e)
                return Isophote(sample, iter+1, False, 3)

            coeffs = coeffs[0]

            # largest harmonic in absolute value drives the correction.
            largest_harmonic_index = np.argmax(np.abs(coeffs[1:]))
            largest_harmonic = coeffs[1:][largest_harmonic_index]

            # see if the amplitude decreased; if yes, keep the
            # corresponding sample for eventual later use.
            if abs(largest_harmonic) < minimum_amplitude_value:
                minimum_amplitude_value = abs(largest_harmonic)
                minimum_amplitude_sample = sample

            # check if converged
            model = first_and_second_harmonic_function(values[0], coeffs)
            residual = values[2] - model

            if ((conver * sample.sector_area * np.std(residual))
                    > np.abs(largest_harmonic)):
                # Got a valid solution. But before returning, ensure
                # that a minimum of iterations has run.
                if iter >= minit-1:
                    sample.update()
                    return Isophote(sample, iter+1, True, 0)

            # it may not have converged yet, but the sample contains too
            # many invalid data points: return.
            if sample.actual_points < (sample.total_points * fflag):
                # when too many data points were flagged, return the
                # best fit sample instead of the current one.
                minimum_amplitude_sample.update()
                return Isophote(minimum_amplitude_sample, iter+1, True, 1)

            # pick appropriate corrector code.
            corrector = _correctors[largest_harmonic_index]

            # generate *NEW* EllipseSample instance with corrected
            # parameter.  Note that this instance is still devoid of other
            # information besides its geometry.  It needs to be explicitly
            # updated for computations to proceed.  We have to build a new
            # EllipseSample instance every time because of the lazy
            # extraction process used by EllipseSample code. To minimize
            # the number of calls to the area integrators, we pay a
            # (hopefully smaller) price here, by having multiple calls to
            # the EllipseSample constructor.
            sample = corrector.correct(sample, largest_harmonic)
            sample.update()

            # see if any abnormal (or unusual) conditions warrant
            # the change to non-iterative mode, or go-inwards mode.
            proceed, lexceed = self._check_conditions(
                sample, maxgerr, going_inwards, lexceed)

            if not proceed:
                sample.update()
                return Isophote(sample, iter+1, True, -1)

        # Got to the maximum number of iterations. Return with
        # code 2, and handle it as a valid isophote. Use the
        # best fit sample instead of the current one.
        minimum_amplitude_sample.update()
        return Isophote(minimum_amplitude_sample, maxit, True, 2)