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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(pressure, *args, **kwargs):
 r"""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. The bottom of the layer can be specified with a pressure or height above the surface pressure. The bottom defaults to the surface pressure. The depth of the layer can be specified in terms of pressure or height above the bottom of the layer. If the top and bottom of the layer are not in the data, they are interpolated by default. Parameters pressure : array-like Atmospheric pressure profile *args : array-like Atmospheric variable(s) measured at the given pressures heights: array-like, optional Atmospheric heights corresponding to the given pressures. Defaults to using heights calculated from ``p`` assuming a standard atmosphere. bottom : `pint.Quantity`, optional The bottom of the layer as a pressure or height above the surface pressure. Defaults to the highest pressure or lowest height given. depth : `pint.Quantity`, optional The thickness of the layer as a pressure or height above the bottom of the layer. Defaults to 100 hPa. interpolate : bool, optional Interpolate the top and bottom points if they are not in the given data. Defaults to True. Returns ------- `pint.Quantity, pint.Quantity` The pressure and data variables of the layer """

    # Pop off keyword arguments
    heights = kwargs.pop('heights', None)
    bottom = kwargs.pop('bottom', None)
    depth = kwargs.pop('depth', 100 * units.hPa)
    interpolate = kwargs.pop('interpolate', True)

    # If we get the depth kwarg, but it's None, set it to the default as well
    if depth is None:
        depth = 100 * units.hPa

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

    # If the bottom is not specified, make it the surface pressure
    if bottom is None:
        bottom = np.nanmax(pressure) * pressure.units

    bottom_pressure, bottom_height = _get_bound_pressure_height(pressure, bottom,
                                                                heights=heights,
                                                                interpolate=interpolate)

    # Calculate the top if whatever units depth is in
    if depth.dimensionality == {'[length]': -1.0, '[mass]': 1.0, '[time]': -2.0}:
        top = bottom_pressure - depth
    elif depth.dimensionality == {'[length]': 1}:
        top = bottom_height + depth
    else:
        raise ValueError('Depth must be specified in units of length or pressure')

    top_pressure, _ = _get_bound_pressure_height(pressure, top, heights=heights,
                                                 interpolate=interpolate)

    ret = []  # returned data variables in layer

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

    # Mask based on top and bottom pressure
    inds = (_less_or_close(pressure, bottom_pressure)
            & _greater_or_close(pressure, top_pressure))
    p_interp = pressure[inds]

    # Interpolate pressures at bounds if necessary and sort
    if interpolate:
        # If we don't have the bottom or top requested, append them
        if not np.any(np.isclose(top_pressure, p_interp)):
            p_interp = np.sort(np.append(p_interp, top_pressure)) * pressure.units
        if not np.any(np.isclose(bottom_pressure, p_interp)):
            p_interp = np.sort(np.append(p_interp, bottom_pressure)) * pressure.units

    ret.append(p_interp[::-1])

    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 = log_interpolate_1d(p_interp, pressure, datavar)
            datavar = datavar_interp
        else:
            datavar = datavar[inds]

        ret.append(datavar[::-1])
    return ret