code: def test_max_pool():
      def verify_max_pool(
          x_shape, pool_size, strides, padding, ceil_mode
      ):
          x = relay.var("x", relay.TensorType(x_shape, "float32"))
          y = tvm.relay.nn.max_pool2d(
              x,
              pool_size=pool_size,
              strides=strides,
              padding=padding,
              ceil_mode=ceil_mode,
          )
          func = relay.Function([x], y)
          x_data = np.random.uniform(size=x_shape).astype("float32")
          verify_results(
              func, [x_data], "test_max_pool", rtol=1e-5, atol=1e-5
          )
  
      verify_max_pool(
          (1, 4, 16, 16),
          pool_size=(2, 2),
          strides=(2, 2),
          padding=(0, 0),
          ceil_mode=False,
      )
  
imports: import pytest
  import numpy
  import onnxruntime
  import tvm
  from tvm import relay
  from tvm.contrib.target.onnx import to_onnx
  from tvm.relay.testing import run_infer_type
label_desc: Use image node_id 12 for calling a global function with example usage: test_max_pool() without return types