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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_layer_heights(heights, depth, *args, **kwargs):
 """Return an atmospheric layer from upper air data with the requested bottom and depth. This function will subset an upper air dataset to contain only the specified layer using the heights only. Parameters heights : array-like Atmospheric heights depth : `pint.Quantity` The thickness of the layer *args : array-like Atmospheric variable(s) measured at the given pressures bottom : `pint.Quantity`, optional The bottom of the layer interpolate : bool, optional Interpolate the top and bottom points if they are not in the given data. Defaults to True. with_agl : bool, optional Returns the heights as above ground level by subtracting the minimum height in the provided heights. Defaults to False. Returns ------- `pint.Quantity, pint.Quantity` The height and data variables of the layer """

    bottom = kwargs.pop('bottom', None)
    interpolate = kwargs.pop('interpolate', True)
    with_agl = kwargs.pop('with_agl', False)

    # Make sure pressure and datavars are the same length
    for datavar in args:
        if len(heights) != len(datavar):
            raise ValueError('Height and data variables must have the same length.')

    # If we want things in AGL, subtract the minimum height from all height values
    if with_agl:
        sfc_height = np.min(heights)
        heights = heights - sfc_height

    # If the bottom is not specified, make it the surface
    if bottom is None:
        bottom = heights[0]

    # Make heights and arguments base units
    heights = heights.to_base_units()
    bottom = bottom.to_base_units()

    # Calculate the top of the layer
    top = bottom + depth

    ret = []  # returned data variables in layer

    # Ensure heights are sorted in ascending order
    sort_inds = np.argsort(heights)
    heights = heights[sort_inds]

    # Mask based on top and bottom
    inds = _greater_or_close(heights, bottom) & _less_or_close(heights, top)
    heights_interp = heights[inds]

    # Interpolate heights at bounds if necessary and sort
    if interpolate:
        # If we don't have the bottom or top requested, append them
        if top not in heights_interp:
            heights_interp = np.sort(np.append(heights_interp, top)) * heights.units
        if bottom not in heights_interp:
            heights_interp = np.sort(np.append(heights_interp, bottom)) * heights.units

    ret.append(heights_interp)

    for datavar in args:
        # Ensure that things are sorted in ascending order
        datavar = datavar[sort_inds]

        if interpolate:
            # Interpolate for the possibly missing bottom/top values
            datavar_interp = interpolate_1d(heights_interp, heights, datavar)
            datavar = datavar_interp
        else:
            datavar = datavar[inds]

        ret.append(datavar)
    return ret