code: def test_broadcast_concat_shape_int64(executor_kind):
      x_shape = (1, 2, 1, 1)
      broadcast_shape = [1, 2, 2, 1]
      x = relay.var("data", relay.TensorType(x_shape, "float32"))
      broadcast_to = relay.op.broadcast_to(
          x, relay.const(broadcast_shape, dtype="int64")
      )
      concate = relay.op.concatenate((broadcast_to,), axis=0)
  
      f = relay.Function([x], concate)
  
      x = np.zeros(x_shape).astype("float32")
      ref_res = np.concatenate(
          (np.broadcast_to(x, broadcast_shape),), axis=0
      )
  
      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 8 for calling a global function with example usage: test_broadcast_concat_shape_int64(executor_kind) without return types