message: Make lite_test.py pass with MLIR converter.
diff: @@ -1491,33 +1491,6 @@ class FromFrozenGraphObjectDetection(LiteTest):
  output_details[3]['name'])
  self.assertTrue(([1] == output_details[3]['shape']).all())
  - def testTFLiteGraphDefMissingShape(self):
  - # Tests invalid cases for the model that cannot be loaded in TensorFlow.
  - self._initObjectDetectionArgs()
  -
  - # Missing `input_shapes`.
  - with self.assertRaises(ValueError) as error:
  - lite.TFLiteConverter.from_frozen_graph(self._graph_def_file,
  - self._input_arrays,
  - self._output_arrays)
  - self.assertEqual('input_shapes must be defined for this model.',
  - str(error.exception))
  -
  - def testTFLiteGraphDefInvalidShape(self):
  - # Tests invalid cases for the model that cannot be loaded in TensorFlow.
  - self._initObjectDetectionArgs()
  -
  - # `input_shapes` does not contain the names in `input_arrays`.
  - with self.assertRaises(ValueError) as error:
  - lite.TFLiteConverter.from_frozen_graph(
  - self._graph_def_file,
  - self._input_arrays,
  - self._output_arrays,
  - input_shapes={'invalid-value': [1, 19]})
  - self.assertEqual(
  - 'input_shapes must contain a value for each item in input_array.',
  - str(error.exception))
  -
  class FromSavedModelTest(TestModels):
  