op_VNO    uint32 = 0xE76B // 	VRR-c	VECTOR NOR
	op_VO     uint32 = 0xE76A // 	VRR-c	VECTOR OR
	op_VPK    uint32 = 0xE794 // 	VRR-c	VECTOR PACK
	op_VPKLS  uint32 = 0xE795 // 	VRR-b	VECTOR PACK LOGICAL SATURATE
	op_VPKS   uint32 = 0xE797 // 	VRR-b	VECTOR PACK SATURATE
	op_VPERM  uint32 = 0xE78C // 	VRR-e	VECTOR PERMUTE
	op_VPDI   uint32 = 0xE784 // 	VRR-c	VECTOR PERMUTE DOUBLEWORD IMMEDIATE

  // Verify fix: this only verifies that shape inference runs and completes on
  // this input, rather than refining any shapes.
  func.func @operand_pack_unranked(%arg0: tensor<*xf32>) -> () {
   // CHECK: tf.Pack
   %outputs_0 = "tf.Pack"(%arg0) {axis = 0 : i64, device = ""} : (tensor<*xf32>) -> tensor<*xf32>

github.com/prometheus/prometheus v0.52.1/go.mod h1:3z74cVsmVH0iXOR5QBjB7Pa6A0KJeEAK5A6UsmAFb1g=
github.com/prometheus/tsdb v0.7.1/go.mod h1:qhTCs0VvXwvX/y3TZrWD7rabWM+ijKTux40TwIPHuXU=
github.com/quic-go/qpack v0.4.0 h1:Cr9BXA1sQS2SmDUWjSofMPNKmvF6IiIfDRmgU0w1ZCo=
github.com/quic-go/qpack v0.4.0/go.mod h1:UZVnYIfi5GRk+zI9UMaCPsmZ2xKJP7XBUvVyT1Knj9A=
github.com/quic-go/quic-go v0.44.0 h1:So5wOr7jyO4vzL2sd8/pD9Kesciv91zSk8BoFngItQ0=

  %0 = "tf.InvertPermutation"(%arg0) : (tensor<8x8xi32>) -> tensor<8x8xi32>
  func.return %0 : tensor<8x8xi32>
}

// -----

// Valid Pack operation.
func.func @testPack(%arg0: tensor<4x8xf32>, %arg1: tensor<4x8xf32>) -> tensor<*xf32> {
  %0 = "tf.Pack"(%arg0, %arg1) {axis = 1 : i64} : (tensor<4x8xf32>, tensor<4x8xf32>) -> tensor<*xf32>
  func.return %0 : tensor<*xf32>
}


// -----

    // Use `PackOp` for scalar concatenation because `ConcatV2Op` doesn't
    // support scalar concatenation.
    if (is_scalar) {
      auto pack = CreateTfOp<PackOp>(rewriter, op, result_type, args,
                                     rewriter.getI64IntegerAttr(axis));
      return pack.getResult();
    }

    // New concatenation axis.
    auto axis_type = tensorflow::GetTypeFromTFTensorShape(

  }];
}



def UnrollBatchMatMulPass : Pass<"tf-unroll-batch-matmul", "mlir::func::FuncOp"> {
  let summary = "Unroll TF BatchMatMul op into Reshape, Slice, MatMul, Pack ops.";
  let constructor = "TF::CreateUnrollBatchMatMulPassPass()";
}

def ClusterFormationPass : Pass<"tf-device-cluster-formation", "mlir::ModuleOp"> {

      Value clamped_index = rewriter.create<mhlo::ClampOp>(
          op.getLoc(), cast_type, clamp_min, cast_op, clamp_max);
      start_indices_vector.push_back(clamped_index);
    }

    // Pack individual start indices to start indices tensor.
    Type start_indices_type = RankedTensorType::get(
        {static_cast<int64_t>(start_indices_vector.size())},
        signed_start_indices_element_type);

  %8 = "tf.FloorMod"(%7, %4) {device = ""} : (tensor<1x1x2x1xi32>, tensor<i32>) -> tensor<1x1x2x1xi32>
  %9 = "tf.FloorDiv"(%arg1, %4) {device = ""} : (tensor<1x1x2x1xi32>, tensor<i32>) -> tensor<1x1x2x1xi32>
  %10 = "tf.Pack"(%2, %9, %8, %2) {axis = 0 : i64, device = ""} : (tensor<1x1x2x1xi32>, tensor<1x1x2x1xi32>, tensor<1x1x2x1xi32>, tensor<1x1x2x1xi32>) -> tensor<4x1x1x2x1xi32>

using ComputedQueryFn = function_ref<bool(ValuePort)>;
using ValueQueryFn = function_ref<Attribute(const ValuePort&)>;
using ValuePortInputs = SmallVectorImpl<ValuePort>;

// Note: Following implements the rank 1 pack op case so could be
// generalized.
//
// Maps the specified component in the `port` of the given op's result to one of
// the element in the input.