}

	// First compute inlinability of all functions in the package.
	inline.CanInlineFuncs(pkg.Funcs, inlProfile)

	// Now we make a second pass to do devirtualization and inlining of
	// calls. Order here should not matter.
	for _, fn := range pkg.Funcs {
		DevirtualizeAndInlineFunc(fn, inlProfile)
	}

	if base.Flag.LowerL != 0 {
		// Perform a garbage collection of hidden closures functions that

// After applying the function, a quantize/dequantize functions are created
// where the body of each function contains a specific quantization algorithm.
// The input of the quantize function has one operand of
// IsValueWithQuantizablePrecision and the output is a tensor with supported
// quantized precision (like int8). For dequantize function, it is the other way
// around.

			bu.SetFastSymbolization(true)
		}
	}

	functions := map[profile.Function]*profile.Function{}
	addFunction := func(f *profile.Function) *profile.Function {
		if fp := functions[*f]; fp != nil {
			return fp
		}
		functions[*f] = f
		f.ID = uint64(len(prof.Function)) + 1
		prof.Function = append(prof.Function, f)
		return f
	}

	missingBinaries := false

  public void testTransform_absent() {
    assertEquals(Optional.absent(), Optional.absent().transform(Functions.identity()));
    assertEquals(Optional.absent(), Optional.absent().transform(Functions.toStringFunction()));
  }

  public void testTransform_presentIdentity() {
    assertEquals(Optional.of("a"), Optional.of("a").transform(Functions.identity()));
  }

  public void testTransform_presentToString() {

                                         while_region.getBody()));
    } else if (auto case_op = dyn_cast<CaseOp>(op)) {
      llvm::SmallVector<func::FuncOp, 4> functions;
      case_op.get_branch_functions(functions);
      AnalyzeFunctionalCaseOrIfOp(case_op, functions, backtrack_analysis);
    } else if (auto if_op = dyn_cast<IfOp>(op)) {
      AnalyzeFunctionalCaseOrIfOp(
          if_op, {if_op.then_function(), if_op.else_function()},

	RF_PANIC                          // body of loop is either currently running, or has panicked
	RF_EXHAUSTED                      // iterator function return, i.e., sequence is "exhausted"
	RF_MISSING_PANIC = 4              // body of loop panicked but iterator function defer-recovered it away

/**
 * Provides type discovery via functions that return an object or configure an object accepting a lambda as the last parameter
 * (via [org.gradle.api.Action] or a Kotlin function type, see [gradleConfigureLambdas]).
 *
 * All configured or returned types that appear in [isPublicAndRestricted]-matching will be discovered (for now, regardless of actual function semantics).
 */
internal

  // If set, use the value as the device type and mark the function graph for
  // XLA compilation.
  string xla_compile_device_type;
  // If true, enables moving ops to different devices or moving unsupported ops
  // out of a compilation cluster.
  bool enable_soft_placement = false;
  // If true, a function attribute, `tf._original_func_name`, will be set in
  // functions which contains the corresponding original TF function name.

// gradient functions.
//
// Some gradient functions can accept None arguments for gradients. The
// following maps the operation name to the indices at which the corresponding
// gradient function can accept None values. e.g. FusedBatchNorm outputs 5
// values and hence receives 5 gradient values during backprop. However the
// gradient function uses only the first of those values and ignores the rest.

	outputLen int             // output length for full security
	function  code            // KIMD/KLMD function code
	state     spongeDirection // whether the sponge is absorbing or squeezing
}

func newAsmState(function code) *asmState {
	var s asmState
	s.function = function
	switch function {
	case sha3_224:
		s.rate = 144
		s.outputLen = 28
	case sha3_256:
		s.rate = 136