}

    def "adhoc verification of code generated from ExcludeJsonLogToCode"() {

        def operand0 = moduleId('shake', 'grain')
        def operand1 = moduleId('root', 'rain')
        def operand2 = moduleId('cover', 'cover')
        def operand3 = moduleId('crib', 'lunchroom')
        def operand4 = moduleId('root', 'sort')

	HasSHA1   bool // K{I,L}MD-SHA-1 functions
	HasSHA256 bool // K{I,L}MD-SHA-256 functions
	HasSHA512 bool // K{I,L}MD-SHA-512 functions
	HasSHA3   bool // K{I,L}MD-SHA3-{224,256,384,512} and K{I,L}MD-SHAKE-{128,256} functions
	HasVX     bool // vector facility
	HasVXE    bool // vector-enhancements facility 1
	_         CacheLinePad
}

func init() {
	archInit()
	initOptions()
	processOptions()
}

[[finding_problems]]
== Finding problems with task output caching

Below we describe a step-by-step process that should help shake out any problems with caching in your build.

=== Ensure incremental build works

First, make sure your build does the right thing without the cache.
Run a build twice without enabling the Gradle build cache.

        verifyHasChildren(whenReadyEvaluated, initScriptAppId, 'init', expectedGradleOps)
    }

    def 'no extra executions for composite builds'() {
        // This test does two things:
        // - shake out internal listener registration that isn't using InternalListener.
        //   There are a lost of listeners registered through the methods that we've decorated in the composite build code

// 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> {

// 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
// the stack to the heap, but this requires first having a pointer
// hidden on the stack. Immediately after a stack is scanned, it only
// points to shaded objects, so it's not hiding anything, and the

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

		}
	} 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

TEST_F(NNGradTest, SoftmaxGrad) {
  TensorShape shape({32, 10});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
  auto y = Softmax(scope_, x);
  RunTest(x, shape, y, shape);
}

TEST_F(NNGradTest, SoftmaxRank3Grad) {
  TensorShape shape({32, 1, 10});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
  auto y = Softmax(scope_, x);
  RunTest(x, shape, y, shape);
}

  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);