}
    auto const_op =
        rewriter.create<TF::ConstOp>(loc, new_type, tensor_proto_attr);
    // Add scast op to match quantize -> composition pattern. The added scast
    // is then removed by canonicalization. ([scast - scast] -> [])
    auto scast_op = rewriter.create<quantfork::StorageCastOp>(
        loc, tensor_qtype, const_op.getOutput());
    q_op->replaceAllUsesWith(scast_op);

        GetFilterConstantOp(filter_value);
    auto filter_value_attr =
        mlir::cast<DenseElementsAttr>(filter_constant_op.getValue());
    if (filter_value_attr.getElementType().isF32()) {
      // This is i8 values disguised as f32 (due to the upcast trick). Simply
      // cast them to i8.
      filter_value_attr =
          mlir::cast<DenseFPElementsAttr>(filter_value_attr)

  "$0.isa<DenseElementsAttr>() && "
  "$0.cast<DenseElementsAttr>().getNumElements() == 1 && "
  "(($0.cast<DenseElementsAttr>().getValues<APFloat>()[0].isLargest() && "
  "$0.cast<DenseElementsAttr>().getValues<APFloat>()[0].isNegative()) || "
  "$0.cast<DenseElementsAttr>().getValues<APFloat>()[0].isNegInfinity())">>;

def L2NormValidReduceIndex : Constraint<CPred<
  "L2NormalizeReduceAxis($0, $1.cast<DenseElementsAttr>())">>;

    auto op = cast<ResizeNearestNeighborOp>(src_op);
    auto loc = op.getLoc();
    auto result_ty = mlir::cast<ShapedType>(op.getType());

    auto input = op.getImages();
    auto input_ty = mlir::cast<ShapedType>(input.getType());
    auto input_element_ty = input_ty.getElementType();
    auto out_size = op.getSize();
    auto out_size_ty = mlir::cast<ShapedType>(out_size.getType());

				if cast != nil {
					st.setTemplate(cast, tmpl)
					st.clearTemplateArgs(args)
					cast = nil
				}
				a = nil
				next = tmpl
			case 'T':
				next = st.templateParam()
			case 'E':
				if a == nil {
					st.fail("expected prefix")
				}
				if cast != nil {
					var toTmpl *Template
					if castTempl, ok := cast.To.(*Template); ok {
						toTmpl = castTempl

    // * The 'strides' attribute must exist and is of the form [1, X, Y, 1].
    // * The 'dilations' attribute is optional, but it must be of the form
    //   [1, X, Y, 1] if exists.

    TFConvOpType tf_op = cast<TFConvOpType>(op);
    if (!TFTypeIsFloat32Tensor(tf_op.getInput()) &&
        !(allow_bf16_and_f16_type_legalization_ &&
          TFTypeIsBFloat16OrHalfTensor(tf_op.getInput())))
      return failure();

  %4 = "tf.GatherV2"(%cst_1, %arg1, %cst_0) {batch_dims = 0 : i64, device = "/job:localhost/replica:0/task:0/device:CPU:0"} : (tensor<3xi32>, tensor<i32>, tensor<i32>) -> tensor<i32>
  %5 = "tf.Cast"(%4) {Truncate = false, device = "/job:localhost/replica:0/task:0/device:CPU:0"} : (tensor<i32>) -> tensor<f32>

        return taskContainer.create(addMaps(Cast.uncheckedNonnullCast(options), singletonMap(Task.TASK_NAME, task)));
    }

    public Task task(Map options, Object task) {
        return task(options, task.toString());
    }

    @Override
    public Task task(Map options, String task, Closure configureClosure) {

    auto cond = symbol_table.lookup<mlir::func::FuncOp>(
        while_op->getAttr("cond").cast<mlir::FlatSymbolRefAttr>().getValue());
    AddCallOpInWhileOpRegion(while_op.getCond(), cond);
    while_op->removeAttr("cond");
    auto body = symbol_table.lookup<mlir::func::FuncOp>(
        while_op->getAttr("body").cast<mlir::FlatSymbolRefAttr>().getValue());
    AddCallOpInWhileOpRegion(while_op.getBody(), body);

        loc, tensorflow::GetTypeFromTFTensorShape({1}, shape_dtype), item_rank,
        scalar_zero);
    // Create two slice ops.
    Type element_type = input.getType().cast<TensorType>().getElementType();
    UnrankedTensorType unranked_tensor = UnrankedTensorType::get(element_type);
    Value scalar_minus_one = CreateI32SplatConst(loc, &rewriter, {}, -1);
    TF::SliceOp slice1 =