code: def _precomputed_from_scale(observation_scale, kernel):
      """Extracts expensive precomputed values."""
      if isinstance(
          observation_scale, tf.linalg.LinearOperatorLowerTriangular
      ):
          return {"tril": {"chol_tril": observation_scale.tril}}
      if isinstance(kernel, multitask_kernel.Independent):
          base_kernel_chol_op = observation_scale.operators[0]
          return {
              "independent": {"chol_tril": base_kernel_chol_op.tril}
          }
      if isinstance(kernel, multitask_kernel.Separable):
          kronecker_op, diag_op = observation_scale.operators
          kronecker_orths = [
              {"identity": k.domain_dimension_tensor()}
              if isinstance(k, tf.linalg.LinearOperatorIdentity)
              else {"unitary": k.matrix}
              for k in kronecker_op.operators
          ]
          return {
              "separable": {
                  "kronecker_orths": kronecker_orths,
                  "diag": diag_op.diag,
              }
          }
      # This should not happen.
      raise ValueError(
          "Unexpected values for kernel and observation_scale."
      )
  
imports: import functools
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import cholesky_util
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import mvn_linear_operator
  from tensorflow_probability.python.distributions.internal import stochastic_process_util
  from tensorflow_probability.python.experimental.distributions import multitask_gaussian_process
  from tensorflow_probability.python.experimental.linalg import linear_operator_unitary
  from tensorflow_probability.python.experimental.psd_kernels import multitask_kernel
  from tensorflow_probability.python.internal import batch_shape_lib
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import nest_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.math.psd_kernels.internal import util
label_desc: Use image node_id 7 for calling a global function with example usage: _precomputed_from_scale(observation_scale, kernel) and returns: dict, dict, dict