private
    const val modelLine = """<script type="text/javascript" src="configuration-cache-report-data.js"></script>"""

    /**
     * Returns the header and footer of the html template as a pair.
     */
    fun load(): Pair<String, String> {
        val template = readHtmlTemplate()
        val headerEnd = template.indexOf(modelLine)
        require(headerEnd > 0) {
            "Invalid configuration cache report template!"

	cmd.Stderr = os.Stderr
	out, err = cmd.Output()
	if err != nil {
		log.Fatalf("loading architecture list: %v\n%s", err, out)
	}
	allGOARCH := make(map[string]bool)
	for _, pair := range strings.Split(strings.TrimSpace(string(out)), "\n") {
		GOARCH := strings.Split(pair, "/")[1]
		allGOARCH[GOARCH] = true
	}

	for _, pkgName := range pkgs {
		if strings.Contains(pkgName, "/boring") {
			continue
		}

	}
	return typ, nil
}

// Wrapper is a wrapper around the Envoy ConfigDump
// It has extra helper functions for handling any/struct/marshal protobuf pain
type Wrapper struct {
	*admin.ConfigDump
}

// UnmarshalJSON is a custom unmarshaller to handle protobuf pain
func (w *Wrapper) UnmarshalJSON(b []byte) error {
	cd := &admin.ConfigDump{}
	err := protomarshal.UnmarshalAllowUnknownWithAnyResolver(nonStrictResolver, b, cd)

// WITH_STDLIB

fun test() {
    var (x, y) = Pair(1, 2)
    <expr>x = 1</expr>
    consume(x)
}

  llvm::ArrayRef<llvm::StringRef> input_names;
  llvm::ArrayRef<
      std::pair<tensorflow::DataType, tensorflow::PartialTensorShape>>
      input_specs;
  llvm::ArrayRef<llvm::StringRef> input_devices;

  // The following are metadata for outputs.
  llvm::ArrayRef<llvm::StringRef> output_names;
  llvm::ArrayRef<
      std::pair<tensorflow::DataType, tensorflow::PartialTensorShape>>
      output_specs;

    /**
     * Locates the log content starting with the first line that matches the given pattern, or null if no such line.
     *
     * @return a pair containing (content-before-matching-line, content-from-matching-line) or null if no match.
     */
    public @Nullable
    Pair<LogContent, LogContent> splitOnFirstMatchingLine(Pattern pattern) {
        for (int i = 0; i < lines.size(); i++) {
            String line = lines.get(i);

            final String v = e.getValue();
            if (properyPattern.matcher(k).matches() && !v.startsWith(CIPHER_PREFIX)) {
                return new Pair<>(k, CIPHER_PREFIX + cipher.encrypt(v));
            }
            return new Pair<>(k, v);
        }).forEach(e -> {
            if (buf.length() > 0) {
                buf.append('\n');
            }
            buf.append(e.getFirst());

    // TODO(chky): Support cases where index_path is not a single string.
    return mlir::cast<mlir::StringAttr>(index_path[0]).getValue();
  }
  return "";
}

absl::StatusOr<std::pair<tensorflow::DataType, tensorflow::PartialTensorShape>>
ProcessTensorSpec(mlir::TensorType type) {
  tensorflow::DataType dtype;
  TF_RETURN_IF_ERROR(
      ConvertScalarTypeToDataType(type.getElementType(), &dtype));

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

package org.gradle.internal.classpath;

import org.gradle.api.file.RelativePath;
import org.gradle.internal.Pair;
import org.gradle.internal.classloader.TransformingClassLoader;
import org.gradle.internal.classpath.transforms.ClassTransform;
import org.gradle.internal.classpath.types.InstrumentationTypeRegistry;

    return Status(absl::StatusCode::kUnknown, "No Local Device Manager");
  *deviceMgrPtr = device_mgr_.get();
  return absl::OkStatus();
}

Status FakeSession::Run(
    const std::vector<std::pair<std::string, Tensor>>& inputs,
    const std::vector<std::string>& output_names,
    const std::vector<std::string>& target_nodes,
    std::vector<Tensor>* outputs) {
  tensorflow::RunMetadata run_metadata;