code: def create_kernels() -> Sequence[kernels.Kernel]:
      result: List[kernels.Kernel] = [
          # Static kernels:
          kernels.White(),
          kernels.Constant(),
          # Stationary kernels:
          kernels.SquaredExponential(),
          kernels.RationalQuadratic(),
          kernels.Exponential(),
          kernels.Matern12(),
          kernels.Matern32(),
          kernels.Matern52(),
          # sum and product kernels:
          kernels.White() + kernels.Matern12(),
          kernels.White() * kernels.Matern12(),
          # slicing:
          kernels.Matern32(active_dims=slice(None, None, 2)),
          kernels.Matern32(active_dims=[1, 2]),
          # other kernels:
          kernels.Cosine(),
          kernels.Linear(),
          kernels.Polynomial(),
          kernels.Periodic(kernels.Matern32()),
          kernels.ChangePoints(
              [kernels.Matern32(), kernels.Matern32()],
              [0.5],
          ),
          kernels.ArcCosine(),
          kernels.Coregion(output_dim=5, rank=2),
          kernels.Convolutional(kernels.Matern32(), [4, 4], [2, 2]),
          # multioutput:
          kernels.SharedIndependent(kernels.Matern32(), output_dim=5),
          kernels.SeparateIndependent(
              [kernels.Matern32() for _ in range(5)]
          ),
          kernels.LinearCoregionalization(
              [kernels.Matern32() for _ in range(3)], np.ones((5, 3))
          ),
          Categorical(
              non_categorical_kernel=kernels.RBF(lengthscales=0.1),
              categorical_kernel=kernels.RBF(lengthscales=0.1),
              num_labels=3,
          ),
      ]
      return result
  
file_path: GPflow/tests/gpflow/kernels/test_broadcasting.py
imports: from typing import Any, Callable, List, Sequence, Tuple, cast
  import numpy
  import pytest
  import tensorflow
  from check_shapes import check_shape
  from check_shapes import check_shapes
  from numpy.testing import assert_allclose
  import gpflow
  import gpflow.ci_utils
  from gpflow import kernels
  from gpflow.base import AnyNDArray, TensorType
  from gpflow.kernels.categorical import Categorical
label_desc: Use image node_id 1 for calling a global function with example usage: create_kernels() and returns: result