loc, mlir::RankedTensorType::get({}, getElementTypeOrSelf(shape)), shape,
      /*begin=*/
      builder->create<TF::ConstOp>(loc, builder->getI32TensorAttr({idx})),
      /*end=*/
      builder->create<TF::ConstOp>(loc, builder->getI32TensorAttr({idx + 1})),
      /*strides=*/
      builder->create<TF::ConstOp>(loc, builder->getI32TensorAttr({1})),
      /*begin_mask=*/0, /*end_mask=*/0, /*ellipsis_mask=*/0,

            });
      }

      ::mlir::TF::ConstOp reduce_dim_op;
      {
        auto reduce_dim_type = tensorflow::GetTypeFromTFTensorShape(
            {3}, rewriter.getIntegerType(32));
        ::mlir::SmallVector<int32_t, 3> reduce_dim_values = {0, 1, 2};
        reduce_dim_op = rewriter.create<TF::ConstOp>(
            odsLoc, ::mlir::DenseIntElementsAttr::get(reduce_dim_type,

  TensorType zero_point_type = scale_type.clone(rewriter.getI32Type());
  scale = rewriter.create<TF::ConstOp>(
      loc, scale_type,
      DenseFPElementsAttr::get(scale_type,
                               {static_cast<float>(qtype.getScale())}));
  zero_point = rewriter.create<TF::ConstOp>(
      loc, zero_point_type,
      DenseIntElementsAttr::get(zero_point_type,

// converted to an ElementsAttr and then those are packed together using
// tf.Pack op.
struct ConvertConst : public OpConversionPattern<TF::ConstOp> {
  using OpConversionPattern::OpConversionPattern;

  LogicalResult matchAndRewrite(
      TF::ConstOp op, OpAdaptor adaptor,
      ConversionPatternRewriter &rewriter) const override {
    // Verify that the tensor proto contains tensor of type variant and scalar

  if (tfl::IsQuantized(tensor)) {
    auto op = builder.create<tfl::QConstOp>(
        loc, mlir::TypeAttr::get(shaped_type), value);
    return op.getOperation();
  }
  auto op = builder.create<tfl::ConstOp>(loc, value);
  op->setAttr("tfl.is_variable", builder.getUnitAttr());
  if (tensor.quantization && !tensor.quantization->min.empty()) {
    if (auto stats_op =

    //    executing listeners in the order they were added
    // 3. push all the items onto 'onto' and return the new head of the stack
    Listener head = ATOMIC_HELPER.gasListeners(this, Listener.TOMBSTONE);
    Listener reversedList = onto;
    while (head != null) {
      Listener tmp = head;
      head = head.next;
      tmp.next = reversedList;
      reversedList = tmp;

    //    executing listeners in the order they were added
    // 3. push all the items onto 'onto' and return the new head of the stack
    Listener head = ATOMIC_HELPER.gasListeners(this, Listener.TOMBSTONE);
    Listener reversedList = onto;
    while (head != null) {
      Listener tmp = head;
      head = head.next;
      tmp.next = reversedList;
      reversedList = tmp;

void SetAllVarIsInitializedToTrue(Block* block) {
  auto builder = OpBuilder::atBlockBegin(block);
  TF::ConstOp const_true = nullptr;
  for (auto op :
       llvm::make_early_inc_range(block->getOps<TF::VarIsInitializedOp>())) {
    builder.setInsertionPoint(op);
    if (!const_true)
      const_true = builder.create<TF::ConstOp>(
          op.getLoc(),
          DenseIntElementsAttr::get(

	// Output the state machine for each function remaining.
	for _, s := range unit.Textp {
		fnSym := loader.Sym(s)
		_, _, _, lines := d.ldr.GetFuncDwarfAuxSyms(fnSym)

		// Chain the line symbol onto the list.
		if lines != 0 {
			syms = append(syms, lines)
			unitlen += int64(len(d.ldr.Data(lines)))
		}
	}

	if d.linkctxt.HeadType == objabi.Haix {

              edge->src()->name(), ":", edge->src_output());
        }
      }

      Subgraph& dst_subgraph = subgraphs_[dst_func_id];
      // Ignore control edges entering the subgraph. We will lift them onto
      // the enclosing call operators in BuildOutputGraph().
      if (!edge->IsControlEdge()) {
        TF_RETURN_IF_ERROR(
            dst_subgraph.RecordArg(edge, node_images, src_arg_pairs));
      }
    }