TypeToken<?> declaredType = propertyMetadata.getDeclaredType();
            Class<?> serviceType = Cast.uncheckedCast(((ParameterizedType) declaredType.getType()).getActualTypeArguments()[0]);
            visitor.visitServiceReference(propertyName, propertyMetadata.isAnnotationPresent(Optional.class), value, serviceName, Cast.uncheckedCast(serviceType));
        });
    }

        if (canBeViewedAs(type)) {
            return Cast.uncheckedCast(toView(modelNode, ruleDescriptor, true));
        } else {
            return null;
        }
    }

    @Override
    public <T> ModelView<? extends T> asImmutable(ModelType<T> type, MutableModelNode modelNode, ModelRuleDescriptor ruleDescriptor) {
        if (canBeViewedAs(type)) {
            return Cast.uncheckedCast(toView(modelNode, ruleDescriptor, false));

  "SliceDenseIntElementsAttrColumn2D($0.cast<ElementsAttr>(), " # column # " )">;

class SliceDenseIntElementsAttr<string index, string axis> : NativeCodeCall<
  "SliceDenseIntElementsAttr($0.cast<ElementsAttr>(), " # index # ", " # axis # ")">;

// Interior padding attribute based on the TF padding.
def GetInteriorPadding : NativeCodeCall <
  "GetInteriorPadding($0.cast<ElementsAttr>())">;

        @Override
        public THIS value(@Nullable T value) {
            super.value(value);
            return Cast.uncheckedNonnullCast(this);
        }

        @Override
        public THIS value(Provider<? extends T> provider) {
            super.value(provider);
            return Cast.uncheckedNonnullCast(this);
        }

        @Override
        public void set(@Nullable File file) {

  auto op_func_attrs = op.getOpFuncAttrs();
  if (!op_func_attrs.empty()) {
    auto print_key_value = [&](mlir::Attribute attr) {
      auto key_value = mlir::cast<mlir::ArrayAttr>(attr).getValue();
      auto key = key_value[0];
      auto value = key_value[1];

      p << mlir::cast<mlir::StringAttr>(key).getValue();
      p << " = ";
      p << value;
    };

    auto op_func_attr_array = op_func_attrs.getValue();
    p << " {";

 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.gradle.internal.extensions.stdlib

import org.gradle.internal.Cast


fun <T> Any.uncheckedCast(): T =

  }
}

LogicalResult SpaceToBatchNDOp::verify() {
  SpaceToBatchNDOp op = *this;
  const auto input_type = op.getInput().getType().cast<TensorType>();
  const auto block_shape_type = op.getBlockShape().getType().cast<TensorType>();
  const auto paddings_type = op.getPaddings().getType().cast<TensorType>();

  // Check that block_shape has rank 1.
  if (!IsOfRankOrUnranked(op.getBlockShape(), 1)) {

     *   }
     * ```
     * Note that only nullability from "stable" smart cast types is considered. The
     * [spec](https://kotlinlang.org/spec/type-inference.html#smart-cast-sink-stability) provides an explanation on smart cast stability.
     */
    public fun KtExpression.isDefinitelyNull(): Boolean =

    // This is the argument used to refer to the pass in
    // the textual format (on the commandline for example).
    return "quant-replace-cast-hacks-with-tf-xla-ops";
  }
  StringRef getDescription() const final {
    // This is a brief description of the pass.
    return "Replace mixed-type Conv and Matmul cast hacks with TF XLA ops.";
  }

  void runOnOperation() override;
};

// Generates params for the XLA Convolution op.

namespace mlir {
namespace quant {

UnrankedTensorType CreateUnknownShapeFromElementType(Type tensor_type) {
  if (!mlir::cast<TensorType>(tensor_type)) return UnrankedTensorType();
  return UnrankedTensorType::get(
      mlir::cast<TensorType>(tensor_type).getElementType());
}

}  // namespace quant