CPred<"$0.getType().cast<ShapedType>().hasRank() && "
          "$0.getType().cast<ShapedType>().getRank() <= " # n>>;

// Checks if the value has rank 'n'.
class HasRank<int n> : Constraint<
    CPred<"$0.getType().cast<ShapedType>().hasRank() && "
          "$0.getType().cast<ShapedType>().getRank() == " # n>>;

class FloatValueEquals<string val> : Constraint<CPred<
  "FloatValueEquals($0, " # val # ")">>;

    // Input should be rank 4.
    if (!input_ty.hasRank() || input_ty.getRank() != 4) {
      return failure();
    }

    // Check that out_size is rank-1, length-2. Otherwise the size is not legal.
    if (!out_size_ty.hasRank() || out_size_ty.getRank() != 1 ||
        out_size_ty.getShape()[0] != 2) {
      return failure();
    }

    // tensor, for setting depth_multiplier attribute, etc.).
    auto filter = tf_op.getFilter();
    auto filter_type = mlir::dyn_cast<RankedTensorType>(filter.getType());
    if (!filter_type || filter_type.getRank() != 4 ||
        !filter_type.hasStaticShape())
      return failure();

    Value input = tf_op.getInput();
    RankedTensorType input_type =
        mlir::dyn_cast<RankedTensorType>(input.getType());

    // the first `TensorListSetItemOp`.
    if (auto shaped_type = element_shape.getType().dyn_cast<ShapedType>()) {
      if (shaped_type.hasRank() && shaped_type.getRank() == 0) {
        bool element_shape_acquired = false;
        auto uses = op.getResult().getUses();
        for (auto &use : llvm::make_early_inc_range(uses)) {
          if (TF::TensorListSetItemOp set_op =

    TensorType new_shape = RankedTensorType::get(
        {weight_shape[0], weight_shape[1], in_channels, multiplier},
        weight_type.getElementType());

    int cur_rank = weight_type.getRank();

    // Inserts a reshape op.
    auto shape_spec_type =
        RankedTensorType::get({cur_rank}, rewriter.getIntegerType(64));
    auto new_shape_const_attr =

    const int quantization_dimension = GetFilterQuantizationDimension(
        op.getDotDimensionNumbers(),
        mlir::cast<TensorType>(filter_value_attr.getType()).getRank());
    const UniformQuantizedPerAxisType filter_uniform_quantized_type =
        CreateI8F32UniformQuantizedPerAxisType(
            filter_constant_op.getLoc(), *rewriter.getContext(),

    if (matchPattern(op_state.operands[1], m_Constant(&shape_attr))) {
      auto shape_ty =
          op_state.operands[1].getType().dyn_cast<RankedTensorType>();
      if (shape_ty != nullptr && shape_ty.hasRank() && shape_ty.getRank() > 1) {
        llvm::SmallVector<mlir::Attribute, 4> shape;
        int32_t dim_size = 0;
        for (const auto& dim :
             llvm::enumerate(shape_attr.getValues<llvm::APInt>())) {

    val stream2 = connection.newStream(headerEntries("b", "banana"), true)
    connection.writePingAndAwaitPong() // Ensure the GO_AWAY that resets stream2 has been received.
    val sink1 = stream1.getSink().buffer()
    val sink2 = stream2.getSink().buffer()
    sink1.writeUtf8("abc")
    assertFailsWith<IOException> {
      sink2.writeUtf8("abc")
      sink2.flush()
    }.also { expected ->

    @Override
    public AntBuilder ant(Closure configureClosure) {
        return ConfigureUtil.configure(configureClosure, getAnt());
    }

    @Override
    public AntBuilder ant(Action<? super AntBuilder> configureAction) {
        AntBuilder ant = getAnt();
        configureAction.execute(ant);
        return ant;
    }

    @Override

     */
    AntBuilder getAnt();

    /**
     * <p>Creates an additional <code>AntBuilder</code> for this project. You can use this in your build file to execute
     * ant tasks.</p>
     *
     * @return Creates an <code>AntBuilder</code> for this project. Never returns null.
     * @see #getAnt()
     */
    AntBuilder createAntBuilder();

    /**