code: def inducing_point_conditional(
      Xnew: tf.Tensor,
      inducing_variable: InducingPoints,
      kernel: MultioutputKernel,
      f: tf.Tensor,
      *,
      full_cov: bool = False,
      full_output_cov: bool = False,
      q_sqrt: Optional[tf.Tensor] = None,
      white: bool = False,
  ) -> MeanAndVariance:
      """Multi-output GP with fully correlated inducing variables.
      The inducing variables are shaped in the same way as evaluations of K, to allow a default
      inducing point scheme for multi-output kernels.
      The covariance matrices used to calculate the conditional have the following shape:
      - Kuu: [M, L, M, L]
      - Kuf: [M, L, N, P]
      - Kff: [N, P, N, P], [N, P, P], [N, P]
  
      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.
  
      :param f: variational mean, [L, 1]
      :param q_sqrt: standard-deviations or cholesky, [L, 1]  or  [1, L, L]
      """
      posterior = FullyCorrelatedPosterior(
          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 4 for calling a global function with example usage: inducing_point_conditional(Xnew, inducing_variable, kernel, f) and returns: posterior