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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_cmap_data(data, kwargs) -> Tuple[colors.Normalize, np.ndarray]: """Get normalized values to be used with a colormap. Parameters data : array_like cmap_min : Optional[float] or "min" By default 0. If "min", minimum value of the data. cmap_max : Optional[float] By default, maximum value of the data cmap_normalize : str or colors.Normalize Returns ------- normalizer : colors.Normalize normalized_data : array_like """

    norm = kwargs.pop("cmap_normalize", None)
    if norm == "log":
        cmap_max = kwargs.pop("cmap_max", data.max())
        cmap_min = kwargs.pop("cmap_min", data[data > 0].min())
        norm = colors.LogNorm(cmap_min, cmap_max)
    elif not norm:
        cmap_max = kwargs.pop("cmap_max", data.max())
        cmap_min = kwargs.pop("cmap_min", 0)
        if cmap_min == "min":
            cmap_min = data.min()
        norm = colors.Normalize(cmap_min, cmap_max, clip=True)
    return norm, norm(data)