// to unlink an object from the heap, this will shade it.
//
// 2. shade(ptr) prevents a mutator from hiding an object by moving
// the sole pointer to it from its stack into a black object in the
// heap. If it attempts to install the pointer into a black object,
// this will shade it.
//
// 3. Once a goroutine's stack is black, the shade(ptr) becomes
// unnecessary. shade(ptr) prevents hiding an object by moving it from

		}
	} else {
		// Note: can't use rawmem (which avoids zeroing of memory), because then GC can scan uninitialized memory.
		to = mallocgc(tomem, et, true)
		if copymem > 0 && writeBarrier.enabled {
			// Only shade the pointers in old.array since we know the destination slice to
			// only contains nil pointers because it has been cleared during alloc.
			//
			// It's safe to pass a type to this function as an optimization because

	// we finish executing. If a GC is in progress right now, it must
	// keep gp.labels alive, because gp.labels is reachable from gp.
	// If gp were to overwrite gp.labels, the deletion barrier would
	// still shade that pointer, which would preserve it for the
	// in-progress GC, so all is well. Any future GC will see the
	// value we copied when scanning b.tags (heap-allocated).
	// We arrange that the store here is always overwriting a nil,

[[migmvn:common_plugins]]
== Migrating common plugins

Maven and Gradle share a common approach of extending the build through plugins.
Although the plugin systems are very different beneath the surface, they share many feature-based plugins, such as:

* Shade/Shadow
* Jetty
* Checkstyle
* JaCoCo
* AntRun (see further down)

// RUN: tf-opt -split-input-file -verify-diagnostics --tf-shape-inference %s | FileCheck %s

module attributes {tf.versions = {producer = 888 : i32}} {
// CHECK-LABEL: testConv2dShapeValidPadding
func.func @testConv2dShapeValidPadding(%arg0: tensor<1x112x80x128xf32>, %arg1: tensor<128x3x3x128xf32>, %arg2: tensor<128xf32>) -> tensor<1x?x?x128xf32> {

namespace tensorflow {

class XlaDevice : public LocalDevice {
 public:
  // Given a tensor, sets `xla::Shape*` the shape of tensor's representation
  // on device, fully padded. On error, the contents of `xla::Shape*`
  // are undefined.
  typedef std::function<Status(const Tensor&, xla::Shape*)> PaddedShapeFn;

  // Wrapper class to store metadata about the XlaDevice, where it can be

  TensorShape shape({2, 2});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
  auto indices = Const(scope_, {{1}, {0}});
  auto y = GatherNd(scope_, x, indices);
  RunTest(x, shape, y, shape);
}

TEST_F(ArrayGradTest, GatherNdGrad_SliceIndexing_Int64) {
  TensorShape shape({2, 2});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
  auto indices = Cast(scope_, Const(scope_, {{1}, {0}}), DT_INT64);

  }
  func.return
}

// CHECK: %[[SHAPE:[0-9]*]] = "tf.Shape"(%[[ARG_0]])
// CHECK: %[[OP_A:[0-9]*]] = "tf.opA"(%[[SHAPE]])
// CHECK: %[[OP_B:[0-9]*]] = "tf.opB"(%[[SHAPE]], %[[OP_A]])
// CHECK: tf_device.replicate
// CHECK:   tf_device.return %[[SHAPE]], %[[OP_A]], %[[OP_B]]


// CHECK-LABEL: func @nested_ops

  if (pack_axis < 0) {
    pack_axis += rank;
  }

  // Concat out shape.
  for (int i = 0; i < rank; ++i) {
    int64_t dim_size = input_type.getDimSize(i);
    if (i == pack_axis) {
      dim_size *= count;
    }
    concat_out_shape.push_back(dim_size);
  }

  // Pack out shape.
  int j = 0;
  for (int i = 0; i < rank + 1; ++i) {
    if (i == pack_axis) {

  xs.push_back(Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape)));
  auto y = AddN(scope_, xs);
  RunTest(xs, {shape, shape, shape}, {y}, {shape});
}

TEST_F(NaryGradTest, Add) {
  TensorShape x1_shape({3, 2, 5});
  TensorShape x2_shape({2, 5});
  auto x1 = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x1_shape));
  auto x2 = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x2_shape));