if !ok {
			break
		}
		// When building to wasip1, the host runtime might be running on Windows
		// or might expose a remote file system which does not have the concept
		// of inodes. Therefore, we cannot make the assumption that it is safe
		// to skip entries with zero inodes.
		if ino == 0 && runtime.GOOS != "wasip1" {
			continue
		}
		const namoff = uint64(unsafe.Offsetof(syscall.Dirent{}.Name))
		namlen, ok := direntNamlen(rec)

    // most likely some of the partial declustering passes behave differently
    // with --config=cuda because of different HostMemory.  So for now only test
    // the non-CUDA config, under the assumption that regressions with
    // --config=cuda would also be detected as regressions without
    // --config=cuda.

    LOG(INFO) << "Not running "

  // not have a virtual device is assumed to return the same shape across all
  // replicas. Thus it is invariant across replicas.
  // TODO(b/277936694): Remove this assumption and special case.
  replicate_op.walk([&](TF::ShapeOp shape_op) {
    MakeShapeOpInvariant(replicate_op, num_replicas, replicate_block, shape_op);
  });

  Region* replicate_region = &replicate_op.getBody();

		// While processing individual events is highly parallel,
		// the design/implementation of P&F has a couple limitations that
		// make using this assumption in the P&F implementation very
		// inefficient because:
		// - we reserve max(initialSeats, finalSeats) for time of executing
		//   both phases of the request

		//
		// TODO(wojtek-t): For the sake of making progress and initially
		// simplifying the implementation, we approximate (b) for all values
		// as the value for an empty string. The assumption we're making here
		// is that the difference between the actual number of watchers that
		// will be processed, i.e. (a)+(b) above and the one from our
		// approximation i.e. (a)+[(b) for field value of ""] will be small.

  // If not, the same-scale op will not be quantized. This decision is based
  // on the current assumption that the performance gain of the same-scale
  // op itself could not beat the overhead of the quantize and dequantize
  // routines need to be added around that op. When the assumption changes,
  // this policy might change as well.
  bool IsConnectedWithCompsiteFunction(Operation* same_scale_op) const {

	line, col uint32
}

// MakePos returns a new Pos for the given PosBase, line and column.
func MakePos(base *PosBase, line, col uint) Pos { return Pos{base, sat32(line), sat32(col)} }

// TODO(gri) IsKnown makes an assumption about linebase < 1.
// Maybe we should check for Base() != nil instead.

func (pos Pos) Pos() Pos       { return pos }
func (pos Pos) IsKnown() bool  { return pos.line > 0 }

// an object is encrypted using SSE-C or SSE-KMS. Maybe AWS S3
// computes the ETag as MD5 of the encrypted content but there is
// no way to verify this assumption since the encryption happens
// inside AWS S3.
// Therefore, S3 clients must not make any assumption about ETags
// in case of SSE-C or SSE-KMS except that the ETag is well-formed.
//
// To put all of this into a simple rule:
//
//	SSE-S3 : ETag == MD5

        and:
        libObjects.noneRecompiled()
        appObjects.recompiledFile(appSourceFile)
        // Test assumes that the app has multiple source files and only one of them has changed. Verify that assumption
        appObjects.hasFiles(appSourceFile, appOtherSourceFile)

        and:
        install.assertInstalled()
        install.exec().out == "hello planet"

        when:
        run installApp

  void runOnOperation() override;

 private:
  // Whether the value is used as a bias input of another op. Here we assume
  // bias is used immediately by the user. This assumption is always correct
  // after constant folding.
  bool UsedAsBias(Value value) {
    for (auto &use : value.getUses()) {
      auto biases = TFL::GetOpQuantSpec(use.getOwner())->biases_params;