if (one_by_one && trivial_strides && trivial_dilations) {
    return "NHWC";
  }

  // If filter spatial dimensions are unknown or not 1x1 we prefer NCHW, because
  // it's the fastest option on NVIDIA GPUs with cuDNN library support.
  return "NCHW";
}

//===----------------------------------------------------------------------===//
// Conv2dBackpropFilterOp

}

Status ImporterBase::PrepareConvert(const Graph& graph,
                                    std::unique_ptr<GraphDef> graph_def) {
  // TODO(fengliuai): Converting to GraphDef and back is the easiest way to
  // clone a graph.
  // TODO(fengliuai): clone the graph without going to graph_def first.
  if (graph_def == nullptr) {
    graph_def = std::make_unique<GraphDef>();
    graph.ToGraphDef(graph_def.get());

func TestTypeOfTypeOf(t *testing.T) {
	// Check that all the type constructors return concrete *rtype implementations.
	// It's difficult to test directly because the reflect package is only at arm's length.
	// The easiest thing to do is just call a function that crashes if it doesn't get an *rtype.
	check := func(name string, typ Type) {
		if underlying := TypeOf(typ).String(); underlying != "*reflect.rtype" {

* Introducing `tf.types.experimental.AtomicFunction` as the fastest way to perform TF computations in Python.

    * Can be accessed through `inference_fn` property of `ConcreteFunction`s
    * Does not support gradients.