code: def separate_independent_conditional_implementation(
      Kmns: tf.Tensor,
      Kmms: tf.Tensor,
      Knns: tf.Tensor,
      f: tf.Tensor,
      *,
      full_cov: bool = False,
      q_sqrt: Optional[tf.Tensor] = None,
      white: bool = False,
  ) -> MeanAndVariance:
      """
      Multi-output GP with independent GP priors.
  
      Number of latent processes equals the number of outputs (L = 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.
      - See above for the parameters and the return value.
      """
      fs = tf.transpose(f)[:, :, None]  # [P, M, 1]
      # [P, 1, M, M]  or  [P, M, 1]
  
      if q_sqrt is not None:
          q_sqrts = (
              tf.transpose(q_sqrt)[:, :, None]
              if q_sqrt.shape.ndims == 2
              else q_sqrt[:, None, :, :]
          )
          base_conditional_args_to_map = (
              Kmms,
              Kmns,
              Knns,
              fs,
              q_sqrts,
          )  # type: Tuple[tf.Tensor, ...]
  
          def single_gp_conditional(
              t: Tuple[tf.Tensor, ...]
          ) -> (
              MeanAndVariance
          ):  # pragma: no cover - tf.map_fn is invisible to codecov
              Kmm, Kmn, Knn, f, q_sqrt = t
              return base_conditional(
                  Kmn,
                  Kmm,
                  Knn,
                  f,
                  full_cov=full_cov,
                  q_sqrt=q_sqrt,
                  white=white,
              )
  
      else:
          base_conditional_args_to_map = (Kmms, Kmns, Knns, fs)
  
          def single_gp_conditional(
              t: Tuple[tf.Tensor, ...]
          ) -> (
              MeanAndVariance
          ):  # pragma: no cover - tf.map_fn is invisible to codecov
              Kmm, Kmn, Knn, f = t
              return base_conditional(
                  Kmn,
                  Kmm,
                  Knn,
                  f,
                  full_cov=full_cov,
                  q_sqrt=q_sqrt,
                  white=white,
              )
  
      rmu, rvar = tf.map_fn(
          single_gp_conditional,
          base_conditional_args_to_map,
          (default_float(), default_float()),
      )  # [P, N, 1], [P, 1, N, N] or [P, N, 1]
  
      fmu = rollaxis_left(tf.squeeze(rmu, axis=-1), 1)  # [N, P]
  
      if full_cov:
          fvar = tf.squeeze(
              rvar, axis=-3
          )  # [..., 0, :, :]  # [P, N, N]
      else:
          fvar = rollaxis_left(tf.squeeze(rvar, axis=-1), 1)  # [N, P]
  
      return fmu, fvar
  
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 11 for calling a global function with example usage: separate_independent_conditional_implementation(Kmns, Kmms, Knns, f) and returns: fmu,  fvar, base_conditional, base_conditional