parameters: (self, x, df, scale)
return_statement: return _squeeze_output(out)
docstring: Log of the Wishart probability density function.
  
          Parameters
          ----------
          x : array_like
              Quantiles, with the last axis of `x` denoting the components.
              Each quantile must be a symmetric positive definite matrix.
          %(_doc_default_callparams)s
  
          Returns
          -------
          pdf : ndarray
              Log of the probability density function evaluated at `x`
  
          Notes
          -----
          %(_doc_callparams_note)s
docstring_summary: Log of the Wishart probability density function.
docstring_tokens: Log of the Wishart probability density function .
function: def logpdf(self, x, df, scale):
          """
          Log of the Wishart probability density function.
  
          Parameters
          ----------
          x : array_like
              Quantiles, with the last axis of `x` denoting the components.
              Each quantile must be a symmetric positive definite matrix.
          %(_doc_default_callparams)s
  
          Returns
          -------
          pdf : ndarray
              Log of the probability density function evaluated at `x`
  
          Notes
          -----
          %(_doc_callparams_note)s
  
          """
          dim, df, scale = self._process_parameters(df, scale)
          x = self._process_quantiles(x, dim)
  
          # Cholesky decomposition of scale, get log(det(scale))
          C, log_det_scale = self._cholesky_logdet(scale)
  
          out = self._logpdf(x, dim, df, scale, log_det_scale, C)
          return _squeeze_output(out)
function_tokens: def logpdf ( self , x , df , scale ) : dim , df , scale = self . _process_parameters ( df , scale ) x = self . _process_quantiles ( x , dim ) # Cholesky decomposition of scale, get log(det(scale)) C , log_det_scale = self . _cholesky_logdet ( scale ) out = self . _logpdf ( x , dim , df , scale , log_det_scale , C ) return _squeeze_output ( out )