code: def test_convolutional_layer():
      # Test data adapted from https://github.com/onnx/onnx/blob/main/docs/Operators.md#Conv
      # 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(
          [
              [
                  [0.0, 1.0, 2.0, 3.0, 4.0],  # (1, 7, 5) input tensor
                  [5.0, 6.0, 7.0, 8.0, 9.0],
                  [10.0, 11.0, 12.0, 13.0, 14.0],
                  [15.0, 16.0, 17.0, 18.0, 19.0],
                  [20.0, 21.0, 22.0, 23.0, 24.0],
                  [25.0, 26.0, 27.0, 28.0, 29.0],
                  [30.0, 31.0, 32.0, 33.0, 34.0],
              ]
          ]
      ).astype(np.float32)
      weights = np.array(
          [
              [
                  [
                      [
                          1.0,
                          0.0,
                          2.0,
                      ],  # (1, 1, 3, 3) tensor for convolution weights
                      [0.0, 1.0, 0.0],
                      [2.0, 0.0, 1.0],
                  ]
              ]
          ]
      ).astype(np.float32)
      layer = ConvLayer2D([1, 7, 5], [1, 3, 3], [2, 1], weights)
      y = layer.eval_single_layer(x)
      assert np.array_equal(
          y, [[[42, 49, 56], [112, 119, 126], [182, 189, 196]]]
      )
  
imports: import numpy
  import pytest
  from omlt.neuralnet.layer import ConvLayer2D, DenseLayer, IndexMapper, InputLayer, PoolingLayer2D
label_desc: Use image node_id 3 for calling a global function with example usage: test_convolutional_layer() without return types