code: def domain_map(self, F, mu_vector, L_vector):
      # apply softplus to the diagonal of L and mask upper coefficient to
      # make it lower-triangular diagonal matrix whose elements are diagonal
      # elements of L mapped through a softplus
      d = self.dim
  
      # reshape from vector form (..., d * d) to matrix form(..., d, d)
      L_matrix = L_vector.reshape((-2, d, d, -4), reverse=1)
  
      L_diag = F.broadcast_mul(
          F.Activation(
              F.broadcast_mul(L_matrix, F.eye(d)), act_type="softrelu"
          ),
          F.eye(d),
      )
  
      mask = lower_triangular_ones(F, d, offset=1)
  
      L_low = F.broadcast_mul(L_matrix, mask)
  
      return mu_vector, L_diag + L_low
  
imports: import math
  from typing import Optional, Tuple
  import numpy
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from gluonts.mx.linalg_util import lower_triangular_ones
  from .distribution import Distribution, _sample_multiple, getF
  from .distribution_output import DistributionOutput
label_desc: Use image node_id 2 for calling the MultivariateGaussianOutput obj's underlying member method code with example usage: obj.domain_map(F, mu_vector, L_vector) and returns: mu_vector,  unknown