SmallVector<T> Permute(const ArrayRef<T> values,
                       const ArrayRef<int64_t> permutation) {
  SmallVector<T> permuted_values(/*Size=*/values.size(), /*Value=*/T{});
  for (auto [i, permutation_idx] : llvm::enumerate(permutation)) {
    permuted_values[i] = std::move(values[permutation_idx]);
  }
  return permuted_values;
}

}  // namespace mlir::quant

                )
            return False

    if not generated:
        # Replace all occurrences of the regex "2014|2015|2016|2017|2018" with "YEAR"
        pattern = regexs["date"]
        for i, line in enumerate(data):
            data[i], found = pattern.subn("YEAR", line)
            if found != 0:
                break

    # if we don't match the reference at this point, fail
    if ref != data:

    llvm::SmallVector<mlir::TF::TPUPartitionedOutputV2Op, 8>& ops =
        search->second;
    for (auto [replica_id, partitioned_output] : llvm::enumerate(ops)) {
      for (auto [core_id, result] :
           llvm::enumerate(partitioned_output->getResults())) {
        // outputs from replicate op are interleaved:
        // [(replica:0,core:0), (replica:1,core:0), ...,

// instruction. Unused arguments and fields are not explicitly enumerated, and
// should not be listed for clarity. Unused arguments and values should always
// assume the default value for the given type.
//
// optab does not list every valid ppc64 opcode, it enumerates representative
// operand combinations for a class of instruction.  The variable oprange indexes
// all valid ppc64 opcodes.

  std::vector<std::optional<std::vector<int>>> input_shapes_vector;
  TF_RETURN_IF_ERROR(ParseNodeShapes(input_shapes_str, input_shapes_vector));
  arg_shapes.resize(input_shapes_vector.size());
  for (const auto& shape : llvm::enumerate(input_shapes_vector)) {
    if (!shape.value().has_value()) {
      TF_RETURN_IF_ERROR(TensorShapeUtils::MakeShape(
          static_cast<int*>(nullptr), 0, &arg_shapes[shape.index()].shape));
      continue;
    }

  if (dynamic_arg_idx) {
    for (auto idx : dynamic_arg_idx.getValue()) {
      dynamic_arg_idx_set.insert(mlir::dyn_cast<IntegerAttr>(idx).getInt());
    }
  }

  for (auto operand_type_and_idx : llvm::enumerate(op.getOperandTypes())) {
    Type operand_type = operand_type_and_idx.value();
    int index = operand_type_and_idx.index();
    tensorflow::tpu::TPUCompileMetadataProto::Arg* arg = metadata->add_args();

      llvm::SmallDenseMap<Value, int> outputs_replaced;
      SmallVector<Type, 4> output_types;
      output_types.reserve(candidate_op->getNumResults());
      for (const auto& enumerated_result :
           llvm::enumerate(candidate_op->getResults())) {
        Value result = enumerated_result.value();
        Type result_type = result.getType();
        // Add this to the test coverage once we create test ops with none type

            mlir::cast<IntegerAttr>(mirrored_index).getInt());
      }
    }
    auto func =
        llvm::cast<func::FuncOp>(m.lookupSymbol(cluster_func.getFunc()));
    for (auto entry : llvm::enumerate(cluster_func.getOperands())) {
      auto operand = SkipIdentityAndReadVariable(entry.value());
      auto block_arg = mlir::dyn_cast<BlockArgument>(operand);
      if (block_arg && block_arg.getOwner() == &replicate.GetBody()) {

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

package hostutil

import (
	"fmt"
	"os"

	"k8s.io/mount-utils"
)

// FileType enumerates the known set of possible file types.
type FileType string

const (
	// FileTypeBlockDev defines a constant for the block device FileType.
	FileTypeBlockDev FileType = "BlockDevice"

    int64_t &next_id) {
  llvm::SmallVector<ResourceHandleValueAndId, 4> resource_vec;
  llvm::StringRef device = GetDeviceOrEmpty(getOperation());

  for (const auto &iter : llvm::enumerate(getResults())) {
    auto index = iter.index();
    if (mlir::isa<TF::ResourceType>(
            getElementTypeOrSelf(iter.value().getType()))) {
      resource_vec.push_back(GetResourceHandleValueAndIdBase(