EXPECT_EQ(it->getNumOperands(), 24);
  EXPECT_EQ(it->getNumResults(), 1);
  // cifg = false, so input2input is not None.
  EXPECT_FALSE(mlir::isa<NoneType>(it->getOperand(1).getType()));
  // input layer norm is None
  EXPECT_TRUE(mlir::isa<NoneType>(it->getOperand(20).getType()));
  // proj_bias is F32
  EXPECT_TRUE(mlir::cast<RankedTensorType>(it->getOperand(17).getType())
                  .getElementType()

		return errMalformedXML(err)
	}

	parsedType := CompressionType(strings.ToUpper(s))
	if s == "" || parsedType == "NONE" {
		parsedType = noneType
	}

	switch parsedType {
	case noneType, gzipType, bzip2Type, snappyType, s2Type, zstdType, lz4Type:
	default:
		return errInvalidCompressionFormat(fmt.Errorf("invalid compression format '%v'", s))
	}

	*c = parsedType

    return failure();
  }
  lstm_variant->use_projection =
      !mlir::isa<NoneType>(op.getProjectionWeights().getType());
  lstm_variant->use_peephole =
      !mlir::isa<NoneType>(op.getCellToOutputWeights().getType());
  lstm_variant->use_layer_norm =
      !mlir::isa<NoneType>(op.getForgetLayerNormCoefficients().getType());

  *op_property = operator_property::GetOperatorProperty(

      SmallVector<Value, 4> inputs;
      inputs.reserve(candidate_op->getNumOperands());
      for (auto operand : candidate_op->getOperands()) {
        Type operand_type = operand.getType();
        if (mlir::isa<NoneType>(operand_type)) {
          inputs.push_back(operand);
          continue;
        }

        auto ele_type =
            mlir::cast<TensorType>(operand.getType()).getElementType();
        if (auto dq_op =

      inputs.reserve(quantizing_op->getNumOperands());
      for (const auto& operand : quantizing_op->getOperands()) {
        Type operand_type = operand.getType();
        if (operand_type.isa<NoneType>()) {
          inputs.push_back(operand);
          continue;
        }

        Type elem_type = operand_type.cast<TensorType>().getElementType();

      inputs.reserve(op_with_region->getNumOperands());
      for (Value operand : op_with_region->getOperands()) {
        const Type operand_type = operand.getType();
        if (mlir::isa<NoneType>(operand_type)) {
          inputs.push_back(operand);
          continue;
        }

        const Type element_type =
            mlir::cast<TensorType>(operand.getType()).getElementType();

      SmallVector<Value, 4> inputs;
      inputs.reserve(quantizing_op->getNumOperands());
      for (auto operand : quantizing_op->getOperands()) {
        Type operand_type = operand.getType();
        if (operand_type.isa<NoneType>()) {
          inputs.push_back(operand);
          continue;
        }

        auto ele_type = operand.getType().cast<TensorType>().getElementType();
        if (static_cast<const ConcreteT*>(this)

TF_UNION_ACCESS_INSTANCE(impl::String, s);
TF_UNION_ACCESS_INSTANCE(impl::TaggedValueTensor, tensor);
TF_UNION_ACCESS_INSTANCE(impl::TensorSpec, tensor_spec);
#undef TF_UNION_ACCESS_INSTANCE

/// The union accessor for `NoneType`.
template <>
struct TaggedValue::UnionAccess<impl::None> {
  static impl::None& unsafe_reference(TaggedValue& t) {
    return None::GetInstance();
  }

}

LogicalResult EnsureBias(Operation* op, int bias_idx,
                         PatternRewriter& rewriter) {
  auto bias = op->getOperand(bias_idx);

  if (!mlir::isa<NoneType>(bias.getType())) return failure();

  // Proceed to create a zero bias.
  auto output = op->getResult(0);
  auto output_type = mlir::dyn_cast_or_null<RankedTensorType>(output.getType());

  if (op_infos.empty())
    return parser.emitError(loc) << " expects at least one data operand";

  Attribute frame;
  if (parser.parseKeyword("frame") ||
      parser.parseAttribute(frame, NoneType::get(context), "frame_name",
                            result.attributes))
    return failure();

  Type i64 = parser.getBuilder().getIntegerType(64);
  if (parser.parseOptionalKeyword("parallel_iterations")) {