parameters: (self, df, scale, size=1, random_state=None)
return_statement: return _squeeze_output(out)
docstring: Draw random samples from an inverse Wishart distribution.
  
          Parameters
          ----------
          %(_doc_default_callparams)s
          size : integer or iterable of integers, optional
              Number of samples to draw (default 1).
          %(_doc_random_state)s
  
          Returns
          -------
          rvs : ndarray
              Random variates of shape (`size`) + (`dim`, `dim), where `dim` is
              the dimension of the scale matrix.
  
          Notes
          -----
          %(_doc_callparams_note)s
docstring_summary: Draw random samples from an inverse Wishart distribution.
docstring_tokens: Draw random samples from an inverse Wishart distribution .
function: def rvs(self, df, scale, size=1, random_state=None):
          """
          Draw random samples from an inverse Wishart distribution.
  
          Parameters
          ----------
          %(_doc_default_callparams)s
          size : integer or iterable of integers, optional
              Number of samples to draw (default 1).
          %(_doc_random_state)s
  
          Returns
          -------
          rvs : ndarray
              Random variates of shape (`size`) + (`dim`, `dim), where `dim` is
              the dimension of the scale matrix.
  
          Notes
          -----
          %(_doc_callparams_note)s
  
          """
          n, shape = self._process_size(size)
          dim, df, scale = self._process_parameters(df, scale)
  
          # Invert the scale
          eye = np.eye(dim)
          L, lower = scipy.linalg.cho_factor(scale, lower=True)
          inv_scale = scipy.linalg.cho_solve((L, lower), eye)
          # Cholesky decomposition of inverted scale
          C = scipy.linalg.cholesky(inv_scale, lower=True)
  
          out = self._rvs(n, shape, dim, df, C, random_state)
  
          return _squeeze_output(out)
function_tokens: def rvs ( self , df , scale , size = 1 , random_state = None ) : n , shape = self . _process_size ( size ) dim , df , scale = self . _process_parameters ( df , scale ) # Invert the scale eye = np . eye ( dim ) L , lower = scipy . linalg . cho_factor ( scale , lower = True ) inv_scale = scipy . linalg . cho_solve ( ( L , lower ) , eye ) # Cholesky decomposition of inverted scale C = scipy . linalg . cholesky ( inv_scale , lower = True ) out = self . _rvs ( n , shape , dim , df , C , random_state ) return _squeeze_output ( out )