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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 log_interpolate_1d(x, xp, *args, **kwargs):
 r"""Interpolates data with logarithmic x-scale over a specified axis. Interpolation on a logarithmic x-scale for interpolation values in pressure coordintates. Parameters x : array-like 1-D array of desired interpolated values. xp : array-like The x-coordinates of the data points. args : array-like The data to be interpolated. Can be multiple arguments, all must be the same shape as xp. axis : int, optional The axis to interpolate over. Defaults to 0. fill_value: float, optional Specify handling of interpolation points out of data bounds. If None, will return ValueError if points are out of bounds. Defaults to nan. Returns ------- array-like Interpolated values for each point with coordinates sorted in ascending order. Examples -------- array([20.03438638, 24.63955657, 29.24472675]) Notes ----- xp and args must be the same shape. """

    # Pull out kwargs
    fill_value = kwargs.pop('fill_value', np.nan)
    axis = kwargs.pop('axis', 0)

    # Log x and xp
    log_x = np.log(x)
    log_xp = np.log(xp)
    return interpolate_1d(log_x, log_xp, *args, axis=axis, fill_value=fill_value)