code: def t_like(x, nu):
      R
      nu = np.asarray(nu)
      return flib.t(x, nu)
docs: 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.