code: def _mvn_log_prob(mean, covariance, y):
      cholesky_matrix = tf.linalg.cholesky(covariance)
      log_prob = -0.5 * linalg.hpsd_quadratic_form_solvevec(
          covariance, y - mean, cholesky_matrix=cholesky_matrix
      )
      log_prob = log_prob - 0.5 * linalg.hpsd_logdet(
          covariance, cholesky_matrix=cholesky_matrix
      )
      event_dims = ps.shape(mean)[-1]
      return log_prob - dtype_util.as_numpy_dtype(mean.dtype)(
          0.5 * event_dims * np.log(2 * np.pi)
      )
  
imports: import collections
  import functools
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import mvn_tril
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.math import linalg
  from tensorflow_probability.python.math.scan_associative import scan_associative
label_desc: Use image node_id 11 for calling a global function with example usage: _mvn_log_prob(mean, covariance, y) and returns: unknown