parameters: (self, x, mean=None, cov=1, allow_singular=False)
return_statement: return _squeeze_output(out)
docstring: Multivariate normal probability density function.
  
          Parameters
          ----------
          x : array_like
              Quantiles, with the last axis of `x` denoting the components.
          %(_mvn_doc_default_callparams)s
  
          Returns
          -------
          pdf : ndarray
              Probability density function evaluated at `x`
  
          Notes
          -----
          %(_mvn_doc_callparams_note)s
docstring_summary: Multivariate normal probability density function.
docstring_tokens: Multivariate normal probability density function .
function: def pdf(self, x, mean=None, cov=1, allow_singular=False):
          """
          Multivariate normal probability density function.
  
          Parameters
          ----------
          x : array_like
              Quantiles, with the last axis of `x` denoting the components.
          %(_mvn_doc_default_callparams)s
  
          Returns
          -------
          pdf : ndarray
              Probability density function evaluated at `x`
  
          Notes
          -----
          %(_mvn_doc_callparams_note)s
  
          """
          dim, mean, cov = self._process_parameters(None, mean, cov)
          x = self._process_quantiles(x, dim)
          psd = _PSD(cov, allow_singular=allow_singular)
          out = np.exp(self._logpdf(x, mean, psd.U, psd.log_pdet, psd.rank))
          return _squeeze_output(out)
function_tokens: def pdf ( self , x , mean = None , cov = 1 , allow_singular = False ) : dim , mean , cov = self . _process_parameters ( None , mean , cov ) x = self . _process_quantiles ( x , dim ) psd = _PSD ( cov , allow_singular = allow_singular ) out = np . exp ( self . _logpdf ( x , mean , psd . U , psd . log_pdet , psd . rank ) ) return _squeeze_output ( out )