CheckOutputShapes(matmul_op,
                    /*input_shapes*/ {make_shape({3, 2}), make_shape({2, 4})},
                    /*input_tensors*/ {},
                    /*expected_shape*/ make_shape({3, 4}));

  // Infer shape when second operand has unknown shape.
  CheckOutputShapes(matmul_op,
                    /*input_shapes*/ {make_shape({3, 2}), unknown_shape()},
                    /*input_tensors*/ {},

      continue;
    }
    dims.reserve(input_shape.num_dims);
    for (int j = 0; j < input_shape.num_dims; ++j) {
      dims.push_back(c.MakeDim(input_shape.dims[j]));
    }
    c.SetInput(i, c.MakeShape(dims));
  }

  // TODO(bgogul): Handle input_tensors_as_shapes.
  // TODO(bgogul): Handle input_resource_shapes_and_types.

  status->status = c.construction_status();
  if (!status->status.ok()) return;

  std::vector<StackFrame> feed_frames = {{"feed.cc", 10, "alpha"}};
  std::vector<StackFrame> neg_frames = {{"neg.cc", 15, "beta"}};
  feed->node.SetStackTrace(std::make_shared<FrozenStackTrace>(feed_frames));
  neg->node.SetStackTrace(std::make_shared<FrozenStackTrace>(neg_frames));

  TF_Output inputs[] = {{feed, 0}};
  TF_Output outputs[] = {{neg, 0}};
  *func = TF_GraphToFunction(func_graph.get(), name, append_hash, -1,

    std::vector<tensorflow::shape_inference::DimensionHandle> dim_vec;
    dim_vec.reserve(num_dims);
    for (int i = 0; i < num_dims; ++i) {
      dim_vec.push_back(ic->MakeDim(dims[i]));
    }
    return ic->MakeShape(dim_vec);
  } else {
    return ic->UnknownShape();
  }
}

}  // namespace

void TF_GraphSetOutputHandleShapesAndTypes(TF_Graph* graph, TF_Output output,