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()
}

        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

				}
			}
		}()

		// Short-lived goroutines exercise different code paths (goroutines with
		// status _Gdead, for instance). This churn doesn't have behavior that
		// we can test directly, but does help to shake out data races.
		ready.Add(1)
		var churn func(i int)
		churn = func(i int) {
			SetGoroutineLabels(WithLabels(ctx, Labels(t.Name()+"-churn-i", fmt.Sprint(i))))
			if i == 0 {
				ready.Done()

// 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

// Copy the internal data representation of `from` to `to`. `new_dims` and
// `num_new_dims` specify the new shape of the `to` tensor, `type` specifies its
// data type. On success, *status is set to TF_OK and the two tensors share the
// same data buffer.
//
// This call requires that the `from` tensor and the given type and shape (dims
// and num_dims) are "compatible" (i.e. they occupy the same number of bytes).

// Copyright 2024 The Go Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

package p

// This type and the following one will share the same GC shape and size.
type Pointery struct {
	p *Pointery
	x [1024]int
}

type Pointery2 struct {
	p *Pointery2
	x [1024]int
}

// This type and the following one will have the same size.
type Vanilla struct {

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

		runnext := atomic.Loaduintptr((*uintptr)(unsafe.Pointer(&pp.runnext)))
		if tail == atomic.Load(&pp.runqtail) {
			return head == tail && runnext == 0
		}
	}
}

// To shake out latent assumptions about scheduling order,
// we introduce some randomness into scheduling decisions
// when running with the race detector.
// The need for this was made obvious by changing the