//  * c is the index vector dimension
//  * a,b iterate over the updates and indices
//
//  The update needs permuting to be in the form (a,b,d,e,f) so that the update
//  window dims are the trailing dimensions.
//
// To canonicalize the updates above, replace the updates with:
//   transpose(updates, permutation={3,4,0,1,2})
//
// Note: NormalizeIndexVector is assumed to have run on the indices already so

# - tests MUST NOT have [k8s.io] in test names
# - tests MUST NOT use KubeDescribe

set -o errexit
set -o nounset
set -o pipefail

# This will canonicalize the path
KUBE_ROOT=$(cd "$(dirname "${BASH_SOURCE[0]}")"/.. && pwd -P)
source "${KUBE_ROOT}/hack/lib/init.sh"

# Set REUSE_BUILD_OUTPUT=y to skip rebuilding dependencies if present

//   - creating a per-language map, which includes data for the raw base language
//     and its canonicalized variant (if applicable),
//   - expanding entries for the equivalence classes defined in CLDR's
//     languageMatch data.
// The per-language map ensures that typically only a very small number of tags
// need to be considered. The pre-expansion of canonicalized subtags and
// equivalence classes reduces the amount of map lookups that need to be done at

				files = append(files, f)
			}
			doc, err := NewFromFiles(fset, files, importPath, mode)
			if err != nil {
				t.Fatal(err)
			}

			// golden files always use / in filenames - canonicalize them
			for i, filename := range doc.Filenames {
				doc.Filenames[i] = filepath.ToSlash(filename)
			}

			// print documentation
			var buf bytes.Buffer

// objects such as ReplicaSet.  Can use pkg/api/meta to generically get the
// UID, but need some way to generically access the selector and pod labels
// fields.

// Canonicalize normalizes the object after validation.
func (jobStrategy) Canonicalize(obj runtime.Object) {
}

func (jobStrategy) AllowUnconditionalUpdate() bool {
	return true
}

        "@llvm-project//llvm:Support",
        "@llvm-project//mlir:IR",
        "@llvm-project//mlir:Support",
    ],
)

cc_library(
    name = "passes",
    srcs = [
        "passes/canonicalize.cc",
        "passes/decompose.cc",
        "passes/generated_decompose.inc",
        "passes/raise_to_tf.cc",
        "passes/rewrite_quantized_io.cc",
    ],
    hdrs = [
        "passes/passes.h",

  // the random function body before being inlined.
  pass_manager.addNestedPass<mlir::func::FuncOp>(
      mlir::TFL::CreateLegalizeJaxRandomPass());

  // Canonicalize, CSE etc.
  pass_manager.addPass(mlir::mhlo::createStablehloLegalizeToHloPass());
  pass_manager.addNestedPass<mlir::func::FuncOp>(
      mlir::createCanonicalizerPass());

			switch v.Kind() {
			case Pointer, Map:
				return v.pointer()
			default:
				return v.ptr
			}
		}
		addr1 := ptrval(v1)
		addr2 := ptrval(v2)
		if uintptr(addr1) > uintptr(addr2) {
			// Canonicalize order to reduce number of entries in visited.
			// Assumes non-moving garbage collector.
			addr1, addr2 = addr2, addr1
		}

		// Short circuit if references are already seen.
		typ := v1.Type()

	// \\server\share paths are not converted to
	// \\?\UNC\server\share paths because the rules for doing so
	// are less well-specified.
	if len(path) >= 2 && path[:2] == `\\` {
		// Don't canonicalize UNC paths.
		return path
	}
	if !filepath.IsAbs(path) {
		// Relative path
		return path
	}

	const prefix = `\\?`

	pathbuf := make([]byte, len(prefix)+len(path)+len(`\`))

// RUN: tf-opt -tfl-legalize-tensorlist -canonicalize -split-input-file %s | FileCheck %s

// CHECK-LABEL: listReserveScalarShapeI32
func.func @listReserveScalarShapeI32(%arg0: tensor<i32>, %arg1: tensor<i32>) -> tensor<!tf_type.variant<tensor<*xi32>>> {
  %0 = "tf.TensorListReserve"(%arg0, %arg1) : (tensor<i32>, tensor<i32>) -> tensor<!tf_type.variant<tensor<*xi32>>>