class_docstr: Posterior predictive in a conjugate Multi-task GP regression model.
code: def _get_flattened_marginal_distribution(self, index_points=None):
      # This returns a MVN of event size [N * E], where N is the number of tasks
      # and E is the number of index points.
      with self._name_and_control_scope(
          "get_flattened_marginal_distribution"
      ):
          index_points = self._get_index_points(index_points)
          covariance = self._compute_flattened_covariance(index_points)
          loc = self._flattened_conditional_mean_fn(index_points)
          scale = tf.linalg.LinearOperatorLowerTriangular(
              self._cholesky_fn(covariance),
              is_non_singular=True,
              name="GaussianProcessScaleLinearOperator",
          )
          return mvn_linear_operator.MultivariateNormalLinearOperator(
              loc=loc,
              scale=scale,
              validate_args=self._validate_args,
              allow_nan_stats=self._allow_nan_stats,
              name="marginal_distribution",
          )
  
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 19 for calling the MultiTaskGaussianProcessRegressionModel obj's underlying member method code with example usage: obj._get_flattened_marginal_distribution(index_points) and returns: mvn_linear_operator