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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 covariance_ellipse(P, deviations=1):
 """ Returns a tuple defining the ellipse representing the 2 dimensional covariance matrix P. Parameters P : nd.array shape (2,2) covariance matrix deviations : int (optional, default = 1) # of standard deviations. Default is 1. Returns (angle_radians, width_radius, height_radius) """


    U, s, _ = linalg.svd(P)
    orientation = math.atan2(U[1, 0], U[0, 0])
    width = deviations * math.sqrt(s[0])
    height = deviations * math.sqrt(s[1])

    if height > width:
        raise ValueError('width must be greater than height')

    return (orientation, width, height)