code: def test_maxpool_layer():
      # Test data adapted from https://github.com/onnx/onnx/blob/main/docs/Operators.md#MaxPool
      # Public methods to test are eval and kernel_with_input_indexes,
      # but eval must call kernel_with_input_indexes on all output indexes to work,
      # so we only need to test eval.
      x = np.array(
          [
              [
                  [1, 2, 3, 4],
                  [5, 6, 7, 8],
                  [9, 10, 11, 12],
                  [13, 14, 15, 16],
              ]
          ]
      ).astype(np.float32)
      layer = PoolingLayer2D(
          [1, 4, 4], [1, 2, 2], [2, 2], "max", [3, 3], 1
      )
      y = layer.eval_single_layer(x)
      assert np.array_equal(y, [[[11, 12], [15, 16]]])
  
imports: import numpy
  import pytest
  from omlt.neuralnet.layer import ConvLayer2D, DenseLayer, IndexMapper, InputLayer, PoolingLayer2D
label_desc: Use image node_id 4 for calling a global function with example usage: test_maxpool_layer() without return types