code: def test_relu_graph(self):
      batch_size = 1
      X = np.random.randn(batch_size, 1, 3, 2).astype(np.float32)
      graph_def = make_graph(
          [make_node("Relu", ["X"], ["Y"])],
          name="test",
          inputs=[
              make_tensor_value_info(
                  "X", onnx.TensorProto.FLOAT, [batch_size, 1, 3, 2]
              )
          ],
          outputs=[
              make_tensor_value_info(
                  "Y", onnx.TensorProto.FLOAT, [batch_size, 1, 3, 2]
              )
          ],
      )
      model_def = make_model(graph_def, producer_name="relu-test")
      op = core.CreateOperator(
          "Onnxifi",
          ["X"],
          ["Y"],
          onnx_model=model_def.SerializeToString(),
          input_names=["X"],
          output_names=["Y"],
          output_shape_hint_0=[
              ONNXIFI_DATATYPE_FLOAT32,
              batch_size,
              1,
              3,
              2,
          ],
      )
      workspace.FeedBlob("X", X)
      workspace.RunOperatorOnce(op)
      Y = workspace.FetchBlob("Y")
      np.testing.assert_almost_equal(Y, np.maximum(X, 0))
  
imports: import numpy
  import time
  import unittest
  import onnx
  import onnx.defs
  from onnx.backend.base import namedtupledict
  from onnx.helper import make_node, make_graph, make_tensor_value_info, make_model
  from caffe2.proto import caffe2_pb2
  from caffe2.python import core, workspace
  from caffe2.python.models.download import ModelDownloader
  from caffe2.python.onnx.onnxifi import onnxifi_caffe2_net
  from caffe2.python.onnx.tests.test_utils import TestCase
label_desc: Use image node_id 1 for calling the OnnxifiTest obj's underlying member method code with example usage: obj.test_relu_graph() without return types