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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 _get_indexer(self, key):
 """ Get indexer for rasterio array. Parameter --------- key: tuple of int Returns ------- band_key: an indexer for the 1st dimension window: two tuples. Each consists of (start, stop). squeeze_axis: axes to be squeezed np_ind: indexer for loaded numpy array See also -------- indexing.decompose_indexer """

        assert len(key) == 3, 'rasterio datasets should always be 3D'

        # bands cannot be windowed but they can be listed
        band_key = key[0]
        np_inds = []
        # bands (axis=0) cannot be windowed but they can be listed
        if isinstance(band_key, slice):
            start, stop, step = band_key.indices(self.shape[0])
            band_key = np.arange(start, stop, step)
        # be sure we give out a list
        band_key = (np.asarray(band_key) + 1).tolist()
        if isinstance(band_key, list):  # if band_key is not a scalar
            np_inds.append(slice(None))

        # but other dims can only be windowed
        window = []
        squeeze_axis = []
        for i, (k, n) in enumerate(zip(key[1:], self.shape[1:])):
            if isinstance(k, slice):
                # step is always positive. see indexing.decompose_indexer
                start, stop, step = k.indices(n)
                np_inds.append(slice(None, None, step))
            elif is_scalar(k):
                # windowed operations will always return an array
                # we will have to squeeze it later
                squeeze_axis.append(- (2 - i))
                start = k
                stop = k + 1
            else:
                start, stop = np.min(k), np.max(k) + 1
                np_inds.append(k - start)
            window.append((start, stop))

        if isinstance(key[1], np.ndarray) and isinstance(key[2], np.ndarray):
            # do outer-style indexing
            np_inds[-2:] = np.ix_(*np_inds[-2:])

        return band_key, tuple(window), tuple(squeeze_axis), tuple(np_inds)