code: def test_has_multiple_inputs():
      data = relay.var("data")
      out1 = data * relay.expr.const(3.0)
      w0 = relay.var("w0")
      out2 = relay.nn.conv2d(data, w0)
      out = relay.add(out1, out2)
      net = relay.Function(relay.analysis.free_vars(out), out)
      net = bind_inputs(
          net, {"data": (1, 16, 224, 224), "w0": (16, 16, 1, 1)}
      )
      target_ops = [relay.op.get("nn.conv2d")]
      node_list = []
      node_dict = {}
      expr2graph(
          net,
          target_ops,
          node_dict,
          node_list,
          tvm.target.Target("llvm"),
      )
      input_names = ["data"]
      verify_has_multiple_inputs(node_list, 2, input_names, False)
      verify_has_multiple_inputs(node_list, 4, input_names, False)
      verify_has_multiple_inputs(node_list, 5, input_names, True)
  
file_path: tvm/tests/python/autotvm/test_autotvm_graph_tuner_utils.py
imports: import pytest
  import tvm
  from tvm import te
  from tvm import autotvm, relay
  from tvm.relay.testing import synthetic
  from tvm.autotvm.graph_tuner.utils import has_multiple_inputs, get_direct_ancestor, get_in_nodes, get_out_nodes, expr2graph, bind_inputs
  from tvm.autotvm.graph_tuner._base import OPT_OUT_OP
  from tvm.autotvm.graph_tuner.utils.traverse_graph import _replace_device_with_tracing
  from tvm.relay.expr import Call, TupleGetItem, Tuple, Var
label_desc: Use image node_id 2 for calling a global function with example usage: test_has_multiple_inputs() without return types