code: def covgrad(x, mean, cov, allow_singular=False):
      if allow_singular:
          raise NotImplementedError(
              "The multivariate normal pdf is not "
              "differentiable w.r.t. a singular covariance matix"
          )
      J = np.linalg.inv(cov)
      solved = np.matmul(J, np.expand_dims(x - mean, -1))
      return 1.0 / 2 * (generalized_outer_product(solved) - J)
  
file_path: autograd/autograd/scipy/stats/multivariate_normal.py
imports: from __future__ import absolute_import
  import scipy.stats
  import autograd.numpy
  from autograd.numpy.numpy_vjps import unbroadcast_f
  from autograd.extend import primitive, defvjp
label_desc: Use image node_id 2 for calling a global function with example usage: covgrad(x, mean, cov, allow_singular) and returns: unknown