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Complete this python program:
def expect_v2(self, fn=None, args=(), loc=0, scale=1, lb=None, ub=None, conditional=False):
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#Here is the Python code you requested.

    """calculate expected value of a function with respect to the distribution
    location and scale only tested on a few examples
    Parameters
    all parameters are keyword parameters
    fn : function (default: identity mapping)
    Function for which integral is calculated. Takes only one argument.
    args : tuple
    argument (parameters) of the distribution
    lb, ub : numbers
    lower and upper bound for integration, default is set using
    quantiles of the distribution, see Notes
    conditional : boolean (False)
    If true then the integral is corrected by the conditional probability
    of the integration interval. The return value is the expectation
    of the function, conditional on being in the given interval.
    Returns
    expected value : float
    Notes
    This function has not been checked for it\s behavior when the integral is
    not finite. The integration behavior is inherited from scipy.integrate.quad.
    The default limits are lb = self.ppf(1e-9, *args), ub = self.ppf(1-1e-9, *args)
    For some heavy tailed distributions, \alpha\, \cauchy\, \halfcauchy\,
    \levy\, \levy_l\, and for \ncf\, the default limits are not set correctly
    even  when the expectation of the function is finite. In this case, the
    integration limits, lb and ub, should be chosen by the user. For example,
    for the ncf distribution, ub=1000 works in the examples.
    There are also problems with numerical integration in some other cases,
    for example if the distribution is very concentrated and the default limits
    are too large.
    """
    if (fn is None):
        def fun(x, *args):
            return ((loc + (x * scale)) * self._pdf(x, *args))
    else:
        def fun(x, *args):
            return (fn((loc + (x * scale))) * self._pdf(x, *args))
    if (lb is None):
        try:
            lb = self.ppf(1e-09, *args)
        except ValueError:
            lb = self.a
    else:
        lb = max(self.a, ((lb - loc) / (1.0 * scale)))
    if (ub is None):
        try:
            ub = self.ppf((1 - 1e-09), *args)
        except ValueError:
            ub = self.b
    else:
        ub = min(self.b, ((ub - loc) / (1.0 * scale)))
    if conditional:
        invfac = (self._sf(lb, *args) - self._sf(ub, *args))
    else:
        invfac = 1.0
    return (integrate.quad(fun, lb, ub, args=args, limit=500)[0] / invfac)