code: def coregionalization_conditional(
      Xnew: tf.Tensor,
      inducing_variable: MultioutputInducingVariables,
      kernel: LinearCoregionalization,
      f: tf.Tensor,
      *,
      full_cov: bool = False,
      full_output_cov: bool = False,
      q_sqrt: Optional[tf.Tensor] = None,
      white: bool = False,
  ) -> MeanAndVariance:
      """Most efficient routine to project L independent latent gps through a mixing matrix W.
      The mixing matrix is a member of the `LinearCoregionalization` and has shape [P, L].
      The covariance matrices used to calculate the conditional have the following shape:
      - Kuu: [L, M, M]
      - Kuf: [L, M, N]
      - Kff: [L, N] or [L, N, N]
  
      Further reference:
  
      - See `gpflow.conditionals._conditional` for a detailed explanation of
        conditional in the single-output case.
      - See the multioutput notebook for more information about the multioutput framework.
      """
      posterior = LinearCoregionalizationPosterior(
          kernel,
          inducing_variable,
          f,
          q_sqrt,
          whiten=white,
          mean_function=None,
          precompute_cache=None,
      )
      return posterior.fused_predict_f(
          Xnew, full_cov=full_cov, full_output_cov=full_output_cov
      )
  
file_path: GPflow/gpflow/conditionals/multioutput/conditionals.py
imports: from typing import Optional
  import tensorflow
  from check_shapes import check_shapes
  from ...base import MeanAndVariance
  from ...inducing_variables import FallbackSeparateIndependentInducingVariables, FallbackSharedIndependentInducingVariables, InducingPoints, MultioutputInducingVariables, SeparateIndependentInducingVariables, SharedIndependentInducingVariables
  from ...kernels import IndependentLatent, LinearCoregionalization, MultioutputKernel, SeparateIndependent, SharedIndependent
  from ...posteriors import FallbackIndependentLatentPosterior, FullyCorrelatedPosterior, IndependentPosteriorMultiOutput, LinearCoregionalizationPosterior
  from ..dispatch import conditional
label_desc: Use image node_id 5 for calling a global function with example usage: coregionalization_conditional(Xnew, inducing_variable, kernel, f) and returns: posterior