if (!output_ty) return success();

  int64_t expected_output_rank = std::max(x_ty.getRank(), y_ty.getRank());
  if (output_ty.getRank() != expected_output_rank)
    return op.emitOpError()
           << "found invalid output rank, expected " << expected_output_rank
           << " but got " << output_ty.getRank();

  // Check output batch dim with potential broadcasting.

    @VisibleForTesting
    void buildProblem(Diagnostic<? extends JavaFileObject> diagnostic, ProblemSpec spec) {
        spec.id(mapKindToId(diagnostic.getKind()), mapKindToLabel(diagnostic.getKind()), GradleCoreProblemGroup.compilation().java());
        spec.severity(mapKindToSeverity(diagnostic.getKind()));
        addFormattedMessage(spec, diagnostic);
        addDetails(spec, diagnostic);
        addLocations(spec, diagnostic);
    }

			},
		},
	}

	if !cmp.Equal(actual, expect, protocmp.Transform()) {
		t.Errorf("want %s, got %s", expect.String(), actual.String())
	}
}

func TestMetadataListMatcher(t *testing.T) {
	getWant := func(regex string) *matcher.MetadataMatcher {
		return &matcher.MetadataMatcher{
			Filter: "istio_authn",
			Path: []*matcher.MetadataMatcher_PathSegment{
				{
					Segment: &matcher.MetadataMatcher_PathSegment_Key{

  if (!lhs_type.hasRank() || !rhs_type.hasRank()) {
    return rewriter.notifyMatchFailure(op, "unsupported unranked input type");
  }
  if (lhs_type.getRank() < 1 || 2 < lhs_type.getRank() ||
      rhs_type.getRank() < 1 || 2 < rhs_type.getRank()) {
    return rewriter.notifyMatchFailure(
        op,
        "unsupported dot operation type; operands must be vectors or "
        "matrices");
  }

                   << " with broadcast_dims = " << broadcast_dims;
    return nullptr;
  }
  auto larger_broadcast_dims =
      GetI64ElementsAttrForSeq(0, result_type.getRank(), &builder);
  if (x_type.getRank() < y_type.getRank()) {
    if (x_type != result_type) {
      x = builder.create<BroadcastInDimOp>(loc, result_type, x, broadcast_dims);
    }
    if (y_type != result_type) {

        public String getCallableName() {
            return callableName;
        }

        public String getCallerClassName() {
            return callerClassName;
        }

        public CallKind getKind() {
            return kind;
        }

        public boolean isMatchedAtDispatchSite() {
            return isMatchedAtDispatchSite;
        }

        public void markedAsMatchedAtDispatchSite() {

    // 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());

 */

package org.gradle.internal.instrumentation.model;

import org.objectweb.asm.Type;

public interface ParameterInfo {
    String getName();
    Type getParameterType();
    ParameterKindInfo getKind();

    // before broadcasting.
    if (operand_type.getRank() < output_type.getRank()) {
      input = InsertExpandDimsOp(op, rewriter, input, output_type.getRank());
    }

    SmallVector<int32_t> broadcast_shape =
        CastI64ArrayToI32(output_type.getShape()).value();
    TensorType broadcast_shape_type =
        output_type.cloneWith({output_type.getRank()}, rewriter.getI32Type());
    auto broadcast_shape_attr =

    String toString() {
        return "[operation $name]"
    }

    Instant getEnd() {
        def instant = end.get()
        if (instant == null) {
            throw new IllegalStateException("Operation '$name' has not completed yet.")
        }
        return instant
    }

    Duration getDuration() {
        return getEnd() - start
    }

    void completed(Instant when) {