code: def test_invalid_customized_input_shape(x_shape, shape_option):
      model = chainer.Sequential(F.relu)
  
      if isinstance(x_shape, tuple):
          xs = np.zeros(x_shape, dtype=np.float32)
      elif isinstance(x_shape, list):
          xs = tuple(
              np.zeros(shape, dtype=np.float32) for shape in x_shape
          )
      else:
          assert isinstance(x_shape, dict)
          xs = {
              k: np.zeros(shape, dtype=np.float32)
              for k, shape in x_shape.items()
          }
  
      with pytest.raises(ValueError):
          export(model, xs, input_shapes=shape_option)
  
imports: import unittest
  import chainer
  import chainer.functions
  import chainer.links
  from chainer import testing
  import numpy
  import pytest
  from onnx_chainer import export
  from onnx_chainer.export import RetainInputHook
  from onnx_chainer.testing import input_generator
  from onnx_chainer_tests.helper import ONNXModelTest
label_desc: Use image node_id 1 for calling a global function with example usage: test_invalid_customized_input_shape(x_shape, shape_option) without return types