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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 _recenter_epsf(self, epsf_data, epsf, centroid_func=centroid_com, box_size=5, maxiters=20, center_accuracy=1.0e-4):
 """ Calculate the center of the ePSF data and shift the data so the ePSF center is at the center of the ePSF data array. Parameters epsf_data : 2D `~numpy.ndarray` A 2D array containing the ePSF image. epsf : `EPSFModel` object The ePSF model. centroid_func : callable, optional A callable object (e.g. function or class) that is used to calculate the centroid of a 2D array. The callable must accept a 2D `~numpy.ndarray`, have a ``mask`` keyword and optionally an ``error`` keyword. The callable object must return a tuple of two 1D `~numpy.ndarray`\\s, representing the x and y centroids. The default is `~photutils.centroids.centroid_com`. recentering_boxsize : float or tuple of two floats, optional The size (in pixels) of the box used to calculate the centroid of the ePSF during each build iteration. If a single integer number is provided, then a square box will be used. If two values are provided, then they should be in ``(ny, nx)`` order. The default is 5. maxiters : int, optional The maximum number of recentering iterations to perform. The default is 20. center_accuracy : float, optional The desired accuracy for the centers of stars. The building iterations will stop if the center of the ePSF changes by less than ``center_accuracy`` pixels between iterations. The default is 1.0e-4. Returns ------- result : 2D `~numpy.ndarray` The recentered ePSF data. """


        # Define an EPSFModel for the input data.  This EPSFModel will be
        # used to evaluate the model on a shifted pixel grid to place the
        # centroid at the array center.
        epsf = EPSFModel(data=epsf_data, origin=epsf.origin, normalize=False,
                         oversampling=epsf.oversampling)

        epsf.fill_value = 0.0
        xcenter, ycenter = epsf.origin

        dx_total = 0
        dy_total = 0
        y, x = np.indices(epsf_data.shape, dtype=np.float)

        iter_num = 0
        center_accuracy_sq = center_accuracy ** 2
        center_dist_sq = center_accuracy_sq + 1.e6
        center_dist_sq_prev = center_dist_sq + 1
        while (iter_num < maxiters and
               center_dist_sq >= center_accuracy_sq):

            iter_num += 1

            # extract a cutout from the ePSF
            slices_large, slices_small = overlap_slices(epsf_data.shape,
                                                        box_size,
                                                        (ycenter, xcenter))
            epsf_cutout = epsf_data[slices_large]
            mask = ~np.isfinite(epsf_cutout)

            # find a new center position
            xcenter_new, ycenter_new = centroid_func(epsf_cutout, mask=mask)
            xcenter_new += slices_large[1].start
            ycenter_new += slices_large[0].start

            # calculate the shift
            dx = xcenter - xcenter_new
            dy = ycenter - ycenter_new
            center_dist_sq = dx**2 + dy**2
            if center_dist_sq >= center_dist_sq_prev:  # don't shift
                break
            center_dist_sq_prev = center_dist_sq

            # Resample the ePSF data to a shifted grid to place the
            # centroid in the center of the central pixel.  The shift is
            # always performed on the input epsf_data.
            dx_total += dx    # accumulated shifts for the input epsf_data
            dy_total += dy
            epsf_data = epsf.evaluate(x=x, y=y, flux=1.0,
                                      x_0=xcenter + dx_total,
                                      y_0=ycenter + dy_total,
                                      use_oversampling=False)

        return epsf_data