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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 heal(self, channel=0, method="linear", fill_value=np.nan, verbose=True):
 """ Remove nans from channel using interpolation. Parameters channel : int or str (optional) Channel to heal. Default is 0. method : {'linear', 'nearest', 'cubic'} (optional) The interpolation method. Note that cubic interpolation is only possible for 1D and 2D data. See `griddata`__ for more information. Default is linear. fill_value : number-like (optional) The value written to pixels that cannot be filled by interpolation. Default is nan. verbose : bool (optional) Toggle talkback. Default is True. __ http://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.griddata.html .. note:: Healing may take several minutes for large datasets. Interpolation time goes as nearest, linear, then cubic. """

        warnings.warn("heal", category=wt_exceptions.EntireDatasetInMemoryWarning)
        timer = wt_kit.Timer(verbose=False)
        with timer:
            # channel
            if isinstance(channel, int):
                channel_index = channel
            elif isinstance(channel, str):
                channel_index = self.channel_names.index(channel)
            else:
                raise TypeError("channel: expected {int, str}, got %s" % type(channel))
            channel = self.channels[channel_index]
            values = self.channels[channel_index][:]
            points = [axis[:] for axis in self._axes]
            xi = tuple(np.meshgrid(*points, indexing="ij"))
            # 'undo' gridding
            arr = np.zeros((len(self._axes) + 1, values.size))
            for i in range(len(self._axes)):
                arr[i] = xi[i].flatten()
            arr[-1] = values.flatten()
            # remove nans
            arr = arr[:, ~np.isnan(arr).any(axis=0)]
            # grid data wants tuples
            tup = tuple([arr[i] for i in range(len(arr) - 1)])
            # grid data
            out = griddata(tup, arr[-1], xi, method=method, fill_value=fill_value)
            self.channels[channel_index][:] = out
        # print
        if verbose:
            print(
                "channel {0} healed in {1} seconds".format(
                    channel.name, np.around(timer.interval, decimals=3)
                )
            )