code: def export_maxpool_3d_dilations_use_ref_impl_large() -> None:
      x_shape = (32, 32, 32)
      dilations = (2, 2, 2)
      kernel_shape = (5, 5, 5)
      strides = (3, 3, 3)
      ceil_mode = True
  
      node = onnx.helper.make_node(
          "MaxPool",
          inputs=["x"],
          outputs=["y"],
          kernel_shape=kernel_shape,
          strides=strides,
          dilations=dilations,
          ceil_mode=ceil_mode,
      )
  
      x = np.random.randn(1, 1, *x_shape).astype(np.float32)
      out_shape, pads = get_output_shape_explicit_padding(
          None,
          x_shape,
          kernel_shape,
          strides,
          dilations,
          ceil_mode=ceil_mode,
      )
      padded = np.pad(
          x,
          (
              (0, 0),
              (0, 0),
              (pads[0], pads[3]),
              (pads[1], pads[4]),
              (pads[2], pads[5]),
          ),
          mode="constant",
          constant_values=0,
      )
      y = pool(
          padded,
          (1, 1, *x_shape),
          kernel_shape,
          strides,
          out_shape,
          "MAX",
          pads,
          dilations=dilations,
      )
  
      expect(
          node,
          inputs=[x],
          outputs=[y],
          name="test_maxpool_3d_dilations_use_ref_impl_large",
      )
  
imports: import numpy
  import onnx
  from onnx.backend.test.case.base import Base
  from onnx.backend.test.case.node import expect
  from onnx.reference.ops.op_pool_common import get_output_shape_auto_pad, get_output_shape_explicit_padding, get_pad_shape, pool
label_desc: Use image node_id 18 for calling the MaxPool obj's underlying member method code with example usage: obj.export_maxpool_3d_dilations_use_ref_impl_large() without return types