code: def bivariate_normal(x, mu, cov):
      """
      Bivariate Gaussian distribution for equal shape *X*, *Y*.
  
      See `bivariate normal
      <http://mathworld.wolfram.com/BivariateNormalDistribution.html>`_
      at mathworld.
      """
      X, Y = np.transpose(x)
      mux, muy = mu
      sigmax, sigmaxy, tmp, sigmay = np.ravel(cov)
      sigmax, sigmay = np.sqrt(sigmax), np.sqrt(sigmay)
      Xmu = X - mux
      Ymu = Y - muy
  
      rho = sigmaxy / (sigmax * sigmay)
      z = (
          Xmu**2 / sigmax**2
          + Ymu**2 / sigmay**2
          - 2 * rho * Xmu * Ymu / (sigmax * sigmay)
      )
      denom = 2 * np.pi * sigmax * sigmay * np.sqrt(1 - rho**2)
      return np.exp(-z / (2 * (1 - rho**2))) / denom
  
imports: import numpy
  from scipy import special
  from statsmodels.sandbox.distributions.multivariate import mvstdtprob
  from .extras import mvnormcdf
label_desc: Use image node_id 3 for calling a global function with example usage: bivariate_normal(x, mu, cov) and returns: unknown