};
std::unique_ptr<TargetHardwareOperation> CreateArithmeticOp() {
  return std::make_unique<CpuArithmeticOp>();
}

// Currently used for these ops:
// tfl.concatenation / tfl.reshape / tfl.pack
class CpuConcatOp : public TargetHardwareOperation {
  double GetOpCost(mlir::Operation* op) const override {
    float cost = 0.0;
    int64_t count;

    }

/** 
 * This is the same as {@link jcifs.smb1.util.Hexdump#toHexString(int val, int
 * size)} but provides a more practical form when trying to avoid {@link
 * java.lang.String} concatenation and {@link java.lang.StringBuffer}.
 */ 
    public static void toHexChars( int val, char dst[], int dstIndex, int size ) {
        while( size > 0 ) {
            int i = dstIndex + size - 1;

                // We assume that the declaration is not empty.
                line.startsWith("DEFAULT_JVM_OPTS='") && line.endsWith('\'') -> {
                    ++replacementsCount
                    // Use shell's string concatenation: '...'"..." glues contents of quoted and double-quoted strings together.
                    // The result would be something like DEFAULT_JVM_OPTS='"-Xmx64m" "-Xms64m"'" \"-javaagent:$APP_HOME/lib/agents/foobar.jar\""

			return true
		}
	}
	return false
}

// IsNamedType reports whether t is the named type with the given package path
// and one of the given names.
// This function avoids allocating the concatenation of "pkg.Name",
// which is important for the performance of syntax matching.
func IsNamedType(t types.Type, pkgPath string, names ...string) bool {
	n, ok := aliases.Unalias(t).(*types.Named)
	if !ok {
		return false

// Programs using telemetry should call Open exactly once.
func Open() {
	counter.Open()
}

// CountFlags creates a counter for every flag that is set
// and increments the counter. The name of the counter is
// the concatenation of prefix and the flag name.
//
//	For instance, CountFlags("gopls/flag:", *flag.CommandLine)
func CountFlags(prefix string, fs flag.FlagSet) {
	fs.Visit(func(f *flag.Flag) {
		New(prefix + f.Name).Inc()
	})
}

import java.lang.reflect.Modifier;
import java.util.Hashtable;
import java.util.Map;

/**
 * A cache of introspection information for a specific class instance.
 * Keys {@link Method} objects by a concatenation of the
 * method name and the names of classes that make up the parameters.
 */
class ClassMap {
    private static final class CacheMiss {}

    private static final CacheMiss CACHE_MISS = new CacheMiss();

    }
    return true;
  }
};
std::unique_ptr<TargetHardwareOperation> CreateArithmeticOp() {
  return std::make_unique<GpuArithmeticOp>();
}

// Currently used for these ops:
// tfl.concatenation / tfl.reshape
class GpuConcatOp : public TargetHardwareOperation {
  double GetOpCost(mlir::Operation* op) const override {
    int64_t count;
    if (ArithmeticCountUtilHelper::GetInputTensorTotalSize(op, &count))

	hsha1 := sha1.New()
	for _, slice := range slices {
		hsha1.Write(slice)
	}
	return hsha1.Sum(nil)
}

// md5SHA1Hash implements TLS 1.0's hybrid hash function which consists of the
// concatenation of an MD5 and SHA1 hash.
func md5SHA1Hash(slices [][]byte) []byte {
	md5sha1 := make([]byte, md5.Size+sha1.Size)
	hmd5 := md5.New()
	for _, slice := range slices {
		hmd5.Write(slice)
	}

func.func @testConcatInvalidAxis(%arg0: tensor<1x2xi32>, %arg1: tensor<1x2xi32>) -> tensor<2x2xi32> {
  // expected-error @+1 {{'tfl.concatenation' op concatenation dimension must be in [-rank, rank)}}
  %0 = "tfl.concatenation"(%arg0, %arg1) {axis = 2 : i32, fused_activation_function = "NONE"} : (tensor<1x2xi32>, tensor<1x2xi32>) -> tensor<2x2xi32>
  func.return %0 : tensor<2x2xi32>
}

// -----

    %cst = arith.constant dense<[2, 100]> : tensor<2xi64>
    %0 = "tfl.concatenation"(%arg0, %arg1) {axis = 0 : i32, fused_activation_function = "NONE"} : (tensor<100xf32>, tensor<100xf32>) -> tensor<200xf32>
    %1 = "tfl.reshape"(%0, %cst) : (tensor<200xf32>, tensor<2xi64>) -> tensor<2x100xf32>