#ifndef TENSORFLOW_COMPILER_MLIR_LITE_STABLEHLO_ODML_CONVERTER_FOLDERS_H_
#define TENSORFLOW_COMPILER_MLIR_LITE_STABLEHLO_ODML_CONVERTER_FOLDERS_H_

namespace mlir::odml {

// Populates the pattern set with all folding patterns. These patterns
// are intended to have precedence over any other patterns added to the set.
void PopulateFolderPatterns(RewritePatternSet &patternSet);

}  // namespace mlir::odml

			}
			logger, _ := ktesting.NewTestContext(t)
			rcInstance.actualStateOfWorld.MarkVolumeAsAttached(logger, volumeName, volumeSpec, nodeName, "")

			rcInstance.populatorHasAddedPods = func() bool {
				// Mark DSW populated to allow unmounting of volumes.
				return true
			}
			// Mark devices paths as reconciled to allow unmounting of volumes.
			rcInstance.volumesNeedUpdateFromNodeStatus = nil

			// Act 2 - reconcile once

func (rc *reconciler) readyToUnmount() bool {
	// During kubelet startup, all volumes present on disk are added as uncertain to ASW.
	// Allow unmount only when DSW is fully populated to prevent unmounting volumes that
	// did not reach DSW yet.
	if !rc.populatorHasAddedPods() {
		return false
	}

	// Allow unmount only when ASW device paths were corrected from node.status to prevent

		goVers = "devel"
	}

	progPath = info.Path
	if progPath == "" {
		progPath = strings.TrimSuffix(filepath.Base(os.Args[0]), ".exe")
	}

	// Main module version information is not populated for the cmd module, but
	// we can re-use the Go version here.
	if IsToolchainProgram(progPath) {
		progVers = goVers
	} else {
		progVers = info.Main.Version

        callee_name_attr.getValue().str());
    result.mutable_method()->mutable_no_quantization();
    return result;
  } else {
    return std::nullopt;
  }
}

// Populates quantized ops from `module_op` to `results`. After going through
// the quantization passes, quantized ops are represented as `func::CallOp` with
// a callee's prefix of `quantized_`.

     *
     * @param node the dependency node
     */
    void addNode(Node node) {
        nodes.add(node);
    }

    /**
     * Adds a dependency to the result. This method populates the {@link #nodes}, {@link #paths},
     * {@link #dispatchedPaths} and {@link #dependencies} collections with the given arguments.
     *
     * @param node the dependency node

            "description": "dataSourceRef specifies the object from which to populate the volume with data, if a non-empty volume is desired. This may be any object from a non-empty API group (non core object) or a PersistentVolumeClaim object. When this field is specified, volume binding will only succeed if the type of the specified object matches some installed volume populator or dynamic provisioner. This field will replace the functionality of the dataSource field and as...

#include "mlir/Support/LogicalResult.h"  // from @llvm-project
#include "tensorflow/core/framework/versions.pb.h"
#include "tensorflow/core/platform/statusor.h"

namespace tensorflow {

// Populates the tf.versions attribute on a module, given a corresponding
// graph VersionDef proto.
void PopulateTfVersions(mlir::ModuleOp module, const VersionDef& versions);

We have focused on the use case where users have an organization-wide remote build cache that is populated regularly by continuous integration builds.
Developers and other continuous integration agents should load cache entries from the remote build cache.
We expect that developers will not be allowed to populate the remote build cache, and all continuous integration builds populate the build cache after running the `clean` task.

#include "tensorflow/core/framework/node_def_util.h"
#include "tensorflow/core/framework/op_def_builder.h"
#include "tensorflow/core/platform/status.h"

namespace tensorflow {

// Extracts the attributes of a MLIR operation and populates the converted
// attributes in a proto map<string, AttrValue>.
Status GetAttrValuesFromOperation(
    mlir::Operation* inst, llvm::StringRef name,
    const tensorflow::OpRegistrationData* op_reg_data,