return emptyManagedFieldsOnErr(DecodeManagedFields(liveAccessor.GetManagedFields()))
	}

	// If the object doesn't have metadata, we should just return without trying to
	// set the managedFields at all, so creates/updates/patches will work normally.
	newAccessor, err := meta.Accessor(newObj)
	if err != nil {
		return nil, err
	}

	if isResetManagedFields(newAccessor.GetManagedFields()) {
		return NewEmptyManaged(), nil
	}

    /**
     * Selects the type of path where to place the given dependency.
     * This method returns one of the values specified in the given collection.
     * This method does not handle the patch-module paths, because the patches
     * depend on which modules have been previously added on the module-paths.
     *
     * <p>If the dependency can be a constituent of both the class-path and the module-path,

  rpm2cpio "devtoolset-10-gcc-10.2.1-11.2.el7.src.rpm" |cpio -idmv
  tar -xvf "gcc-10.2.1-20210130.tar.xz" --strip 1
  ;;
esac

# Apply the devtoolset patches to gcc.
/rpm-patch.sh "gcc.spec"

./contrib/download_prerequisites

mkdir -p "${TARGET}-build"
cd "${TARGET}-build"

"${TARGET}-src/configure" \
      --prefix="${TARGET}/usr" \

			return nil, err
		}
	}

	out := make(map[string]any)
	err = yaml.Unmarshal([]byte(by), &out)
	if err != nil {
		return nil, err
	}
	return out, nil
}

// OverlayYAML patches the overlay tree over the base tree and returns the result. All trees are expressed as YAML
// strings.
func OverlayYAML(base, overlay string) (string, error) {
	if strings.TrimSpace(base) == "" {
		return overlay, nil

  echo
  echo "  Set the DRY_RUN environment var to skip git push and creating PR."
  echo "  This is useful for creating patches to a release branch without making a PR."
  echo "  When DRY_RUN is set the script will leave you in a branch containing the commits you cherry-picked."
  echo

potentially exploit a vulnerability in the TensorFlow code that handles videos,
which could allow them to execute arbitrary code on the system running
TensorFlow.

It is important to keep TensorFlow up to date with the latest security patches
and follow the sandboxing guideline above to protect against these types of
vulnerabilities.

## Security properties of execution modes

TensorFlow has several execution modes, with Eager-mode being the default in v2.