code: def test_kuf_shape(
      inducing_variable_P: InducingVariables,
      inducing_variable_L: InducingVariables,
      kernel: Kernel,
  ) -> None:
      if isinstance(kernel, mk.LinearCoregionalization):
          inducing_variable = inducing_variable_L
      else:
          inducing_variable = inducing_variable_P
  
      Kuf = covariances.Kuf(inducing_variable, kernel, Datum.Xnew)
  
      if isinstance(kernel, mk.SharedIndependent):
          if isinstance(
              inducing_variable, mf.SeparateIndependentInducingVariables
          ):
              assert Kuf.shape == (Datum.P, Datum.M, Datum.N)
          else:
              assert Kuf.shape == (Datum.M, Datum.N)
      elif isinstance(kernel, mk.LinearCoregionalization):
          assert Kuf.shape == (Datum.L, Datum.M, Datum.N)
      else:
          assert Kuf.shape == (Datum.P, Datum.M, Datum.N)
  
imports: from typing import Any, Callable, Sequence
  import numpy
  import pytest
  import tensorflow
  import gpflow
  import gpflow.inducing_variables.multioutput
  import gpflow.kernels.multioutput
  from gpflow import covariances
  from gpflow.inducing_variables import InducingVariables
  from gpflow.kernels import Kernel
label_desc: Use image node_id 7 for calling a global function with example usage: test_kuf_shape(inducing_variable_P, inducing_variable_L, kernel) without return types