code: def test_conditional_broadcasting(
      full_cov: bool, white: bool, conditional_type: str
  ) -> None:
      """
      Test that the `conditional` and `sample_conditional` broadcasts correctly
      over leading dimensions of Xnew. Xnew can be shape [..., N, D],
      and conditional should broadcast over the [...].
      """
      q_mu = np.random.randn(Data.M, Data.Dy)
      q_sqrt: AnyNDArray = np.tril(
          np.random.randn(Data.Dy, Data.M, Data.M), -1
      )
  
      if conditional_type == "Z":
          inducing_variable = Data.Z
          kernel = gpflow.kernels.Matern52(lengthscales=0.5)
      elif conditional_type == "inducing_points":
          inducing_variable = gpflow.inducing_variables.InducingPoints(
              Data.Z
          )
          kernel = gpflow.kernels.Matern52(lengthscales=0.5)
      elif conditional_type == "mixing":
          # variational params have different output dim in this case
          q_mu = np.random.randn(Data.M, Data.L)
          q_sqrt = np.tril(np.random.randn(Data.L, Data.M, Data.M), -1)
          inducing_variable = mf.SharedIndependentInducingVariables(
              gpflow.inducing_variables.InducingPoints(Data.Z)
          )
          kernel = mk.LinearCoregionalization(
              kernels=[
                  gpflow.kernels.Matern52(lengthscales=0.5)
                  for _ in range(Data.L)
              ],
              W=Data.W,
          )
      else:
          raise NotImplementedError
  
      if conditional_type == "mixing" and full_cov:
          pytest.skip("combination is not implemented")
  
      num_samples = 5
  
      def sample_conditional_fn(X: tf.Tensor) -> SamplesMeanAndVariance:
          return cast(
              SamplesMeanAndVariance,
              sample_conditional(
                  X,
                  inducing_variable,
                  kernel,
                  tf.convert_to_tensor(q_mu),
                  q_sqrt=tf.convert_to_tensor(q_sqrt),
                  white=white,
                  full_cov=full_cov,
                  num_samples=num_samples,
              ),
          )
  
      samples: AnyNDArray = np.array(
          [sample_conditional_fn(X)[0] for X in Data.SX]
      )
      means: AnyNDArray = np.array(
          [sample_conditional_fn(X)[1] for X in Data.SX]
      )
      variables: AnyNDArray = np.array(
          [sample_conditional_fn(X)[2] for X in Data.SX]
      )
  
      samples_S12, means_S12, vars_S12 = sample_conditional(
          Data.SX,
          inducing_variable,
          kernel,
          tf.convert_to_tensor(q_mu),
          q_sqrt=tf.convert_to_tensor(q_sqrt),
          white=white,
          full_cov=full_cov,
          num_samples=num_samples,
      )
  
      samples_S1_S2, means_S1_S2, vars_S1_S2 = sample_conditional(
          Data.S1_S2_X,
          inducing_variable,
          kernel,
          tf.convert_to_tensor(q_mu),
          q_sqrt=tf.convert_to_tensor(q_sqrt),
          white=white,
          full_cov=full_cov,
          num_samples=num_samples,
      )
  
      assert_allclose(samples_S12.shape, samples.shape)
      assert_allclose(
          samples_S1_S2.shape,
          [Data.S1, Data.S2, num_samples, Data.N, Data.Dy],
      )
      assert_allclose(means_S12, means)
      assert_allclose(vars_S12, variables)
      assert_allclose(
          means_S1_S2.numpy().reshape(
              Data.S1 * Data.S2, Data.N, Data.Dy
          ),
          means,
      )
      if full_cov:
          vars_s1_s2 = vars_S1_S2.numpy().reshape(
              Data.S1 * Data.S2, Data.Dy, Data.N, Data.N
          )
          assert_allclose(vars_s1_s2, variables)
      else:
          vars_s1_s2 = vars_S1_S2.numpy().reshape(
              Data.S1 * Data.S2, Data.N, Data.Dy
          )
          assert_allclose(vars_s1_s2, variables)
  
file_path: GPflow/tests/gpflow/conditionals/test_broadcasted_conditionals.py
imports: from typing import cast
  import numpy
  import pytest
  import tensorflow
  from numpy.testing import assert_allclose
  import gpflow
  import gpflow.inducing_variables.multioutput
  import gpflow.kernels.multioutput
  from gpflow.base import AnyNDArray, SamplesMeanAndVariance
  from gpflow.conditionals import sample_conditional
  from gpflow.conditionals.util import mix_latent_gp
label_desc: Use image node_id 1 for calling a global function with example usage: test_conditional_broadcasting(full_cov, white, conditional_type) and returns: cast