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,

				),
				nil,
			},
			name: "NodeA and nodeB have same topologyKey and label value. NodeA has an existing pod that matches the inter pod affinity rule. The pod can not be scheduled onto nodeA and nodeB but can be scheduled onto nodeC",
		},
		{
			pod: st.MakePod().Namespace("NS1").Labels(map[string]string{"foo": "123"}).PodAntiAffinityIn("foo", "region", []string{"bar"}, st.PodAntiAffinityWithRequiredReq).Obj(),

// 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(

        methodVisitor.visitVarInsn(ALOAD, 1); // put var #1 ('foo') on the stack
        methodVisitor.visitLdcInsn(boxedType); // push the constant Class onto the stack
        methodVisitor.visitInsn(propertyClass.isPrimitive() ? ICONST_1 : ICONST_0); // push int 1 or 0 (interpreted as true or false) onto the stack
        Label startTry = new Label();
        methodVisitor.visitLabel(startTry);