if trimmedText == "" {
		return ""
	}

	const indent = `  `
	inLines := strings.Split(trimmedText, "\n")
	outLines := make([]string, 0, len(inLines))

	for _, line := range inLines {
		outLines = append(outLines, indent+strings.TrimSpace(line))
	}

	return strings.Join(outLines, "\n")

==============================================================================*/

include "mlir/Pass/PassBase.td"

def OutlineCompositesPass: Pass<"outline-composites", "func::FuncOp"> {
  let summary = "Outlines specific patterns into composites.";
  let description = [{
    Outline specific patterns into composites. Specific patterns can be any
    sub-DAG within a single `Block*`. The signature of the new composite

  builder->create<func::ReturnOp>(cluster_return_op.getLoc(),
                                  cluster_return_op.getOperands());
  cluster_return_op.erase();
}

// Builds a function that outlines region attached to cluster_op or launch_op,
// and inserts built function into given module.
template <typename ClusterOrLaunchOp>
func::FuncOp BuildFunction(llvm::ArrayRef<Value> live_ins, ClusterOrLaunchOp op,

		goldenLine = strings.Join(strings.Fields(goldenLine), " ")
		outLine := strings.Join(strings.Fields(outLines[i]), " ")
		if outLine != goldenLine {
			t.Fatalf("line %d differs: got:\n\t%s\nwant:\n\t%s", i+1, outLine, goldenLine)
		}
	}
	if len(goldenLines) != len(outLines) {
		t.Fatalf("output longer than golden; first extra output line %d: %q\n", len(goldenLines)+1, outLines[len(goldenLines)])
	}
}

namespace mlir {
namespace TFL {
namespace {
#define GEN_PASS_DEF_WHILEOUTLINEPASS
#include "tensorflow/compiler/mlir/lite/transforms/passes.h.inc"

// This pass outlines the cond/body region of the TFL WhileOp into functions and
// replaces the regions with calls to these outlined functions.
class WhileOutlinePass : public impl::WhileOutlinePassBase<WhileOutlinePass> {
 public:
  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(WhileOutlinePass)

weeks prior to a new release. The `istio/istio.io` repository does not get branched until the release is ready
for publication.

This document outlines getting in new features after a new branch has been cut and the process for getting a PR
merged in before and after the first public release.

# Feature Freeze

using mlir::SymbolUserOpInterface;
using mlir::func::FuncOp;

#define GEN_PASS_DEF_XLACLUSTERFORMATIONPASS
#include "tensorflow/compiler/mlir/tf2xla/internal/passes/clustering_passes.h.inc"

// Outlines partitioned call ops with `_XlaMustCompile` to device clusters.
struct XlaClusterFormationPass
    : public impl::XlaClusterFormationPassBase<XlaClusterFormationPass> {
  void runOnOperation() override;
};

}

// ordered list of all available outline colors
var outlines = [
    "outline-blue",
    "outline-red",
    "outline-blueviolet",
    "outline-darkolivegreen",
    "outline-fuchsia",
    "outline-sienna",
    "outline-gold",
    "outline-orangered",
    "outline-teal",
    "outline-maroon",
    "outline-black"
];

// state: which value is outlined this color?
var outlined = {};

import org.gradle.launcher.daemon.server.api.HandleStop
import org.gradle.test.precondition.Requires
import org.gradle.test.preconditions.IntegTestPreconditions
import org.gradle.test.preconditions.UnitTestPreconditions

/**
 * Outlines the lifecycle of the daemon given different sequences of events.
 *
 * These tests are a little different due to their async nature. We use the classes

 public:
  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(QuantizeVariablesPass)
  explicit QuantizeVariablesPass() = default;

  void runOnOperation() override;

 private:
  // Outlines the regions of the WhileOp's cond and body and insert function
  // calls instead.
  void QuantizeVariable(OpBuilder &builder,
                        const std::vector<VarHandleOp> &var_handle_op);
};