func::CallOp to_graph) {
  int64_t total_size_transferred = 0;
  for (auto input : to_graph.getOperands()) {
    Operation* input_op = input.getDefiningOp();
    if (input_op && input_op == from_graph.getOperation()) {
      auto input_type =
          mlir::dyn_cast_or_null<RankedTensorType>(input.getType());
      if (input_type == nullptr || !input_type.hasStaticShape()) continue;

                                     StringRef func_name,
                                     Value& func_input_arg) {
  Operation* input_op = input_val.getDefiningOp();

  Operation* insertion_point = input_op->getParentOfType<func::FuncOp>();
  if (!insertion_point) insertion_point = input_op->getParentOfType<ModuleOp>();
  rewriter.setInsertionPointAfter(insertion_point);

  UnrankedTensorType create_unknown_input_shape =

    // mlir constant op.
    Operation* input_op = operand.getDefiningOp();
    if (input_op == nullptr) return failure();

    Attribute attr;
    if (matchPattern(input_op, m_Constant(&attr))) {
      // Constant case.
      builder->setInsertionPoint(op);
      Operation* duplicated_input_op = builder->clone(*input_op);

      // Rewire the inputs.

  MutableArrayRef<OpOperand> inputs =
      replicate.GetOperandsForBlockArgument(replicate_arg);
  for (const auto& entry : llvm::enumerate(inputs)) {
    auto input_op = entry.value().get().getDefiningOp();
    if (!input_op || !IsSupportedInputOp(input_op, resource_alias_analysis))
      return false;
  }
  OpBuilder builder = CreateBuilderAfterOp(compile_launch);

	if !p.more() {
		return EOF
	}
	tok := p.input[p.inputPos]
	p.inputPos++
	return tok
}

func (p *Parser) back() {
	if p.inputPos == 0 {
		p.errorf("internal error: backing up before BOL")
	} else {
		p.inputPos--
	}
}

func (p *Parser) peek() lex.ScanToken {
	if p.more() {
		return p.input[p.inputPos].ScanToken
	}
	return scanner.EOF
}

        !result_type || !result_type.getElementType().isSignlessInteger(8)) {
      return failure();
    }

    auto input_op =
        dyn_cast<arith::ConstantOp>(op.getOperand(0).getDefiningOp());
    if (!input_op) {
      return failure();
    }

    Builder builder(op.getContext());
    auto new_gather_op = rewriter.create<TFL::GatherOp>(
        op.getLoc(),

                                     Value input, int32_t block_size,
                                     ArrayRef<int64_t> input_shape) {
  auto input_op = input.getDefiningOp();
  OpBuilder builder(input_op);
  builder.setInsertionPointAfter(input_op);
  SmallVector<int64_t, 4> transform_shape = {
      input_shape[0], input_shape[1] / block_size, input_shape[2] / block_size,

        final List<String> docIdList = new ArrayList<>();
        for (final Map<String, Object> inputDoc : docList) {
            final Object idValue = inputDoc.get(fessConfig.getIndexFieldId());
            if (idValue == null) {
                continue;
            }

            final Object configIdValue = inputDoc.get(fessConfig.getIndexFieldConfigId());
            if (configIdValue == null) {
                continue;

  LogicalResult matchAndRewrite(quantfork::DequantizeCastOp op,
                                PatternRewriter& rewriter) const override {
    auto input_op = op.getArg().getDefiningOp();
    if (auto q = llvm::dyn_cast_or_null<quantfork::QuantizeCastOp>(input_op)) {
      if (!q->getAttr(kVolatileOpAttrName)) return failure();

      if (remove_volatile_ops_type == kPreserveInputsAndOutputs) {

  LogicalResult matchAndRewrite(quantfork::DequantizeCastOp op,
                                PatternRewriter& rewriter) const override {
    auto input_op = op.getArg().getDefiningOp();
    if (auto q = llvm::dyn_cast_or_null<quantfork::QuantizeCastOp>(input_op)) {
      if (!q->getAttr(kVolatileOpAttrName)) return failure();

      // If the quantize op is a requantize op, it is being used in other scale