code: def _inverse_cholesky(P):
      """
      Computes a Cholesky decomposition of the inverse of a posdef matrix.
      """
      # Ref: https://nbviewer.jupyter.org/gist/fehiepsi/5ef8e09e61604f10607380467eb82006#Precision-to-scale_tril
      Lf = ops.cholesky(ops.flip(P, (-2, -1)))
      L_inv = ops.transpose(ops.flip(Lf, (-2, -1)), -2, -1)
      L = ops.triangular_inv(L_inv)
      return L
  
imports: import math
  from collections import OrderedDict, defaultdict
  from contextlib import contextmanager
  from functools import reduce
  import funsor.ops
  from funsor.affine import affine_inputs, extract_affine, is_affine
  from funsor.delta import Delta
  from funsor.domains import Real, Reals
  from funsor.interpretations import compress_gaussians
  from funsor.ops import AddOp, SubOp
  from funsor.tensor import Tensor, align_tensor, align_tensors
  from funsor.terms import Binary, Funsor, FunsorMeta, Number, Slice, Subs, Variable, eager, reflect
  from funsor.util import broadcast_shape, get_tracing_state, lazy_property
label_desc: Use image node_id 8 for calling a global function with example usage: _inverse_cholesky(P) and returns: L