code: def test_broadcast_pool2d_shape_int64(executor_kind):
      x_shape = (1, 3, 32, 32)
      out_shape = (2, 3, 32, 32)
      x = relay.var("data", shape=x_shape, dtype="float32")
      broadcast_to = relay.broadcast_to(
          x, shape=relay.const([2, 3, 32, 32], dtype="int64")
      )
      pool2d = relay.nn.max_pool2d(
          broadcast_to, pool_size=(3, 3), padding=(1, 1, 1, 1)
      )
      sub = relay.subtract(broadcast_to, pool2d)
  
      f = relay.Function([x], sub)
      x = np.ones(x_shape).astype("float32")
      ref_res = np.zeros(out_shape).astype("float32")
  
      for target, dev in tvm.testing.enabled_targets():
          op_res = relay.create_executor(
              executor_kind, device=dev, target=target
          ).evaluate(f)(x)
          tvm.testing.assert_allclose(op_res.numpy(), ref_res)
  
imports: import sys
  import pytest
  import numpy
  import tvm
  import tvm.testing
  import tvm.topi.testing
  from tvm import relay, te, topi
  from tvm.relay import transform
  from tvm.relay.testing import run_infer_type
label_desc: Use image node_id 9 for calling a global function with example usage: test_broadcast_pool2d_shape_int64(executor_kind) without return types