code: def fully_correlated_conditional(
      Kmn: tf.Tensor,
      Kmm: tf.Tensor,
      Knn: tf.Tensor,
      f: tf.Tensor,
      *,
      full_cov: bool = False,
      full_output_cov: bool = False,
      q_sqrt: Optional[tf.Tensor] = None,
      white: bool = False,
  ) -> MeanAndVariance:
      """
      This function handles conditioning of multi-output GPs in the case where the conditioning
      points are all fully correlated, in both the prior and posterior.
  
      :param full_cov: calculate covariance between inputs
      :param full_output_cov: calculate covariance between outputs
      :param white: use whitened representation
      :return: mean, variance
      """
      mean, var = fully_correlated_conditional_repeat(
          Kmn,
          Kmm,
          Knn,
          f,
          full_cov=full_cov,
          full_output_cov=full_output_cov,
          q_sqrt=q_sqrt,
          white=white,
      )
      return tf.squeeze(mean, axis=0), tf.squeeze(var, axis=0)
  
file_path: GPflow/gpflow/conditionals/util.py
imports: from typing import Optional, Tuple
  import tensorflow
  from check_shapes import check_shape
  from check_shapes import check_shapes
  from ..base import MeanAndVariance
  from ..config import default_float, default_jitter
  from ..utilities.ops import leading_transpose
label_desc: Use image node_id 6 for calling a global function with example usage: fully_correlated_conditional(Kmn, Kmm, Knn, f) and returns: tf,  tf