auto type = mlir::dyn_cast<ShapedType>(val.getType());
  if (!type) return false;
  // Supported original tensor data types.
  if (type.getElementType().isF32() || type.getElementType().isBF16())
    return true;
  return false;
}

std::optional<tensorflow::quantization::QuantizationComponentSpec>
GetWeightComponentSpec(
    const tensorflow::quantization::QuantizationOptions& quantization_options) {

    return absl::OkStatus();
  } else if (type.isF32()) {
    *dtype = DT_FLOAT;
    return absl::OkStatus();
  } else if (type.isF64()) {
    *dtype = DT_DOUBLE;
    return absl::OkStatus();
  } else if (type.isBF16()) {
    *dtype = DT_BFLOAT16;
    return absl::OkStatus();
  } else if (type.isFloat8E4M3FN()) {
    *dtype = DT_FLOAT8_E4M3FN;
    return absl::OkStatus();
  } else if (type.isFloat8E5M2()) {

using absl::StatusOr;

namespace errors = tensorflow::errors;

tflite::TensorType ConvertTypeToTensorType(mlir::Type type) {
  if (type.isF16()) {
    return tflite::TensorType_FLOAT16;
  } else if (type.isBF16()) {
    return tflite::TensorType_BFLOAT16;
  } else if (type.isF32()) {
    return tflite::TensorType_FLOAT32;
  } else if (type.isF64()) {
    return tflite::TensorType_FLOAT64;

}

func.func private @add_scatter(%arg0: tensor<bf16>, %arg1: tensor<bf16>) -> tensor<bf16> {
  %0 = "tf.AddV2"(%arg0, %arg1) {device = ""} : (tensor<bf16>, tensor<bf16>) -> tensor<bf16>
  func.return %0 : tensor<bf16>
}

func.func private @ge_select(%arg0: tensor<bf16>, %arg1: tensor<bf16>) -> tensor<i1> {
  %0 = "tf.GreaterEqual"(%arg0, %arg1) {device = ""} : (tensor<bf16>, tensor<bf16>) -> tensor<i1>

  Type type = val_bcast.getType();
  Type elem_type = getElementTypeOrSelf(type);
  // Xla's all_reduce legalizer bitcasts to 32 bits, so only
  // element types size <= 4 bytes are supported.
  if (elem_type.isBF16() || elem_type.isF16() || elem_type.isTF32() ||
      elem_type.isF32()) {
    zero = builder.getFloatAttr(elem_type, 0);
  } else {
    return false;
  }
  if (auto ranked_type = dyn_cast<RankedTensorType>(type)) {

    // FusedMatMul kernel supports limited set of data types.
    Type element_ty = getElementTypeOrSelf(matmul.getType());
    if (!element_ty.isF32() && !element_ty.isBF16()) {
      (void)rewriter.notifyMatchFailure(matmul, [&](Diagnostic &diag) {
        diag << "supported data types for _FusedMatMul are float and bfloat16, "
             << " but got " << element_ty;
      });

// CHECK:           %[[VAL_2:.*]] = "tf.Div"(%[[VAL_0]], %[[VAL_1]]) : (tensor<2x3xf16>, tensor<3xf16>) -> tensor<2x3xf16>
// CHECK:           %[[VAL_3:.*]] = "tf.Div"(%[[VAL_0]], %[[VAL_1]]) : (tensor<2x3xf16>, tensor<3xf16>) -> tensor<2x3xf16>
// CHECK:           %[[VAL_4:.*]] = "tf.FloorDiv"(%[[VAL_0]], %[[VAL_1]]) : (tensor<2x3xf16>, tensor<3xf16>) -> tensor<2x3xf16>

      Type element_type = tensor_type.getElementType();
      if (element_type.isF32()) continue;
      // Element type can either be f32 or bf16 for `SparseMatMulOp` so it
      // must be bf16 here.
      assert(element_type.isBF16());
      Type tensor_type_f32;
      if (tensor_type.hasRank()) {
        tensor_type_f32 = tensorflow::GetTypeFromTFTensorShape(

    return shaped_type.getElementType().isF32();
  }
  return false;
}

// Returns true if it is a shaped type of bf16 elements.
inline bool IsBF16ShapedType(Type t) {
  if (auto shaped_type = t.dyn_cast_or_null<ShapedType>()) {
    return shaped_type.getElementType().isBF16();
  }
  return false;
}

// Returns true if it is a shaped type of FloatType elements.
inline bool IsFloatShapedType(Type t) {

	op_LLGF    uint32 = 0xE316 // FORMAT_RXY1       LOAD LOGICAL (64<-32)
	op_LLGFAT  uint32 = 0xE39D // FORMAT_RXY1       LOAD LOGICAL AND TRAP (64<-32)
	op_LLGFR   uint32 = 0xB916 // FORMAT_RRE        LOAD LOGICAL (64<-32)
	op_LLGFRL  uint32 = 0xC40E // FORMAT_RIL2       LOAD LOGICAL RELATIVE LONG (64<-32)
	op_LLGH    uint32 = 0xE391 // FORMAT_RXY1       LOAD LOGICAL HALFWORD (64)