"$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>())">>;

				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();

        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 =

 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package org.gradle.internal.service;

import org.gradle.api.Action;
import org.gradle.internal.Cast;
import org.gradle.internal.Factory;
import org.gradle.internal.concurrent.CompositeStoppable;
import org.gradle.internal.concurrent.Stoppable;

import javax.annotation.Nullable;
import java.io.Closeable;

  // CHECK-LABEL: add_v2_uint32
  // CHECK:  %[[CAST:.*]] = "tf.Cast"(%arg0) <{Truncate = false}> : (tensor<ui32>) -> tensor<i32>
  // CHECK:  %[[CAST1:.*]] = "tf.Cast"(%arg1) <{Truncate = false}> : (tensor<ui32>) -> tensor<i32>
  // CHECK:  %[[ADD:.*]] = "tf.AddV2"(%[[CAST]], %[[CAST1]]) : (tensor<i32>, tensor<i32>) -> tensor<i32>
  // CHECK:  %[[CAST2:.*]] = "tf.Cast"(%[[ADD]]) <{Truncate = false}> : (tensor<i32>) -> tensor<ui32>

	// Build rules for each service-port.
	for svcName, svc := range proxier.svcPortMap {
		svcInfo, ok := svc.(*servicePortInfo)
		if !ok {
			proxier.logger.Error(nil, "Failed to cast serviceInfo", "serviceName", svcName)
			continue
		}
		protocol := strings.ToLower(string(svcInfo.Protocol()))
		svcPortNameString := svcInfo.nameString

      // even though K doesn't explicitly implement Comparable.
      comparator = (Comparator<? super K>) NATURAL_ORDER;
    }
    if (map instanceof ImmutableSortedMap) {
      // TODO(kevinb): Prove that this cast is safe, even though
      // Collections.unmodifiableSortedMap requires the same key type.
      @SuppressWarnings("unchecked")
      ImmutableSortedMap<K, V> kvMap = (ImmutableSortedMap<K, V>) map;