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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 stretch_hist_equalize(self, approximate=False):
 """Stretch the current image's colors through histogram equalization. Args: approximate (bool):
 Use a faster less-accurate percentile calculation. At the time of writing the dask version of `percentile` is not as accurate as the numpy version. This will likely change in the future. Current dask version 0.17. """

        logger.info("Perform a histogram equalized contrast stretch.")

        nwidth = 2048.
        logger.debug("Make histogram bins having equal amount of data, " +
                     "using numpy percentile function:")

        def _band_hist(band_data):
            cdf = da.arange(0., 1., 1. / nwidth, chunks=nwidth)
            if approximate:
                # need a 1D array
                flat_data = band_data.ravel()
                # replace with nanpercentile in the future, if available
                # dask < 0.17 returns all NaNs for this
                bins = da.percentile(flat_data[da.notnull(flat_data)],
                                     cdf * 100.)
            else:
                bins = dask.delayed(np.nanpercentile)(band_data, cdf * 100.)
                bins = da.from_delayed(bins, shape=(nwidth,), dtype=cdf.dtype)
            res = dask.delayed(np.interp)(band_data, bins, cdf)
            res = da.from_delayed(res, shape=band_data.shape,
                                  dtype=band_data.dtype)
            return res

        band_results = []
        for band in self.data['bands'].values:
            if band == 'A':
                continue
            band_data = self.data.sel(bands=band)
            res = _band_hist(band_data.data)
            band_results.append(res)

        if 'A' in self.data.coords['bands'].values:
            band_results.append(self.data.sel(bands='A'))
        self.data.data = da.stack(band_results,
                                  axis=self.data.dims.index('bands'))