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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 t_like(x, nu):
 R""" Student's T log-likelihood. Describes a zero-mean normal variable whose precision is gamma distributed. Alternatively, describes the mean of several zero-mean normal random variables divided by their sample standard deviation. .. math:: f(x \mid \nu) = \frac{\Gamma(\frac{\nu+1}{2})}{\Gamma(\frac{\nu}{2}) \sqrt{\nu\pi}} \left( 1 + \frac{x^2}{\nu} \right)^{-\frac{\nu+1}{2}} :Parameters: - `x` : Input data. - `nu` : Degrees of freedom. """

    nu = np.asarray(nu)
    return flib.t(x, nu)