return Murmur3_128HashFunction.MURMUR3_128;
  }

  /**
   * Returns a hash function implementing the <a href="https://131002.net/siphash/">64-bit
   * SipHash-2-4 algorithm</a> using a seed value of {@code k = 00 01 02 ...}.
   *
   * @since 15.0
   */
  public static HashFunction sipHash24() {
    return SipHashFunction.SIP_HASH_24;
  }

  /**

    return Murmur3_128HashFunction.MURMUR3_128;
  }

  /**
   * Returns a hash function implementing the <a href="https://131002.net/siphash/">64-bit
   * SipHash-2-4 algorithm</a> using a seed value of {@code k = 00 01 02 ...}.
   *
   * @since 15.0
   */
  public static HashFunction sipHash24() {
    return SipHashFunction.SIP_HASH_24;
  }

  /**

  %0:3 = "tf.ShapeN"(%arg0, %arg1, %arg2) : (tensor<f32>, tensor<1x32x32x16xf32>, tensor<*xf32>) -> (tensor<0xi64>, tensor<4xi64>, tensor<?xi64>)

  // CHECK: return %[[SHAPE0]], %[[SHAPE1]], %[[SHAPE2]]
  func.return %0#0, %0#1, %0#2 : tensor<0xi64>, tensor<4xi64>, tensor<?xi64>
}

// CHECK-LABEL: func @testShapeNToShape
func.func @testShapeNToShape(%arg0: tensor<*xf32>) -> tensor<?xi64> {

  %0:3 = "tf.Split"(%cst, %input) : (tensor<i32>, tensor<4x6xf32>) -> (tensor<4x2xf32>, tensor<4x2xf32>, tensor<4x2xf32>)
  // CHECK: return %[[ONE]], %[[TWO]], %[[THREE]]
  func.return %0#0, %0#1, %0#2 : tensor<4x2xf32>, tensor<4x2xf32>, tensor<4x2xf32>
}

//===----------------------------------------------------------------------===//
// tf.TopKV2 legalization

    %cst = "tf.Const"() {value = dense<[1, 1, 8]> : tensor<3xi32>} : () -> tensor<3xi32>
    %0 = "tf.XlaGather"(%arg0, %arg1, %cst) {dimension_numbers = "\0A\03\00\01\02\1A\01\02", indices_are_sorted = true} : (tensor<1x1x9xi32>, tensor<1xi32>, tensor<3xi32>) -> tensor<*xi32>
    func.return %0 : tensor<*xi32>
  }

  // CHECK-LABEL: func @xla_gather_with_fold

// CHECK:  return %0#0, %0#1, %0#2 : tensor<?x!tf_type.string>, tensor<?xi64>, tensor<?xi64>

  %0:3 = "tfl.unpack"(%arg0) {axis = 1 : i32, num = 3 : i32} : (tensor<1x3x2xf32>) -> (tensor<1x2xf32>, tensor<1x2xf32>, tensor<1x2xf32>)
  %1 = "tfl.concatenation"(%0#0, %0#1, %0#2) {axis = -1 : i32, fused_activation_function = "NONE"} : (tensor<1x2xf32>, tensor<1x2xf32>, tensor<1x2xf32>) -> tensor<1x6xf32>
  func.return %1 : tensor<1x6xf32>

    "mhlo.return"(%1, %arg1, %arg2) : (tensor<i32>, tensor<i32>, tensor<i32>) -> ()
  }) : (tensor<i32>, tensor<i32>, tensor<i32>) -> (tensor<i32>, tensor<i32>, tensor<i32>)
  func.return %0#0, %0#1, %0#2 : tensor<i32>, tensor<i32>, tensor<i32>
}

// -----

// CHECK-LABEL:  func @while_with_reduce(%arg0: tensor<1x256xf32>, %arg1: tensor<1xf32>) -> (tensor<i32>, tensor<i32>, tensor<i32>, tensor<1xf32>) {