class_docstr: Base class for elliptical copula
  
  This class requires subclassing and currently does not have generic
  methods based on an elliptical generator.
  
  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.
docstr: Pearson correlation from kendall's tau.
  
  Parameters
  ----------
  tau : array_like
      Kendall's tau correlation coefficient.
  
  Returns
  -------
  Pearson correlation coefficient for given tau in elliptical
  copula. This can be used as parameter for an elliptical copula.
docstr_tok: ["Pearson","correlation","from","kendall","'s","tau",".","Parameters","--","--","--","--","--","tau",":","array_like","Kendall","'s","tau","correlation","coefficient",".","Returns","--","--","--","-","Pearson","correlation","coefficient","for","given","tau","in","elliptical","copula",".","This","can","be","used","as","parameter","for","an","elliptical","copula","."]
code: def corr_from_tau(self, tau):
      """Pearson correlation from kendall's tau.
  
      Parameters
      ----------
      tau : array_like
          Kendall's tau correlation coefficient.
  
      Returns
      -------
      Pearson correlation coefficient for given tau in elliptical
      copula. This can be used as parameter for an elliptical copula.
      """
      corr = np.sin(tau * np.pi / 2)
      return corr
  
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 6 for calling the EllipticalCopula obj's underlying member method code with example usage: obj.corr_from_tau(tau) and returns: corr