code: def test_infer_inputs(self, backend):
      x_shape = (3, 4, 5)
  
      @make_tf_graph([x_shape])
      def build_model(x):
          return tf.nn.relu(x)
  
      model, inputs, outputs = build_model
      if not isinstance(outputs, (tuple, list)):
          outputs = [outputs]
  
      output_names = [
          j if isinstance(j, str) else j.op.name for j in outputs
      ]
      mlmodel = converter.convert(
          model, outputs=output_names, convert_to=backend[0]
      )
      assert mlmodel is not None
  
      input_values = [random_gen(x_shape, -10.0, 10.0)]
      input_dict = dict(zip(inputs, input_values))
      TensorFlowBaseTest.run_compare_tf(model, input_dict, outputs)
  
imports: import os
  import shutil
  import tempfile
  import numpy
  import pytest
  import coremltools
  import coremltools.converters
  import coremltools.proto.FeatureTypes_pb2
  from coremltools import EnumeratedShapes, ImageType, RangeDim, TensorType
  from coremltools._deps import _HAS_TF_1, _IS_MACOS, MSG_TF1_NOT_FOUND
  from coremltools.converters.mil.frontend.tensorflow.converter import TFConverter
  from coremltools.converters.mil.frontend.tensorflow.test.testing_utils import TensorFlowBaseTest, get_tf_keras_io_names, make_tf_graph
  from coremltools.converters.mil.testing_reqs import backends
  from coremltools.converters.mil.testing_utils import random_gen
label_desc: Use image node_id 3 for calling the TestTfModelInputsOutputs obj's underlying member method code with example usage: obj.test_infer_inputs(backend) and returns: tf