class_docstr: Gaussian copula.
  
  It is constructed from a multivariate normal distribution over
  :math:`\mathbb{R}^d` by using the probability integral transform.
  
  For a given correlation matrix :math:`R \in[-1, 1]^{d \times d}`,
  the Gaussian copula with parameter matrix :math:`R` can be written
  as:
  
  .. math::
  
      C_R^{\text{Gauss}}(u) = \Phi_R\left(\Phi^{-1}(u_1),\dots,
      \Phi^{-1}(u_d) \right),
  
  where :math:`\Phi^{-1}` is the inverse cumulative distribution function
  of a standard normal and :math:`\Phi_R` is the joint cumulative
  distribution function of a multivariate normal distribution with mean
  vector zero and covariance matrix equal to the correlation
  matrix :math:`R`.
  
  Parameters
  ----------
  corr : scalar or array_like
      Correlation or scatter matrix for the elliptical copula. In the
      bivariate case, ``corr` can be a scalar and is then considered as
      the correlation coefficient. If ``corr`` is None, then the scatter
      matrix is the identity matrix.
  k_dim : int
      Dimension, number of components in the multivariate random variable.
  allow_singular : bool
      Allow singular correlation matrix.
      The behavior when the correlation matrix is singular is determined by
      `scipy.stats.multivariate_normal`` and might not be appropriate for
      all copula or copula distribution metnods. Behavior might change in
      future versions.
  
  Notes
  -----
  Elliptical copulas require that copula parameters are set when the
  instance is created. Those parameters currently cannot be provided in the
  call to methods. (This will most likely change in future versions.)
  If non-empty ``args`` are provided in methods, then a ValueError is raised.
  The ``args`` keyword is provided for a consistent interface across
  copulas.
  
  References
  ----------
  .. [1] Joe, Harry, 2014, Dependence modeling with copulas. CRC press.
      p. 163
code: def _arg_from_tau(self, tau):
      # for generic compat
      return self.corr_from_tau(tau)
  
imports: import numpy
  from scipy import stats
  from statsmodels.compat.scipy import multivariate_t
  from statsmodels.distributions.copula.copulas import Copula
label_desc: Use image node_id 3 for calling the GaussianCopula obj's underlying member method code with example usage: obj._arg_from_tau(tau) and returns: self