TableConvertor: printerstorage.TableConvertor{TableGenerator: printers.NewTableGenerator().With(printersinternal.AddHandlers)},
	}
	options := &generic.StoreOptions{
		RESTOptions: optsGetter,
		AttrFunc:    svcreg.GetAttrs,
	}
	if err := store.CompleteWithOptions(options); err != nil {
		return nil, nil, nil, err
	}

	statusStore := *store
	statusStore.UpdateStrategy = svcreg.StatusStrategy

  }

  // Replace old while op with new while op.
  auto new_while_op = builder.create<TF::WhileOp>(
      while_op.getLoc(), new_result_types, new_operands, while_op->getAttrs());
  auto new_while_wrapper =
      CreateIsland(new_while_op, while_wrapper.getControlInputs(), builder);
  for (auto result : while_wrapper.getOutputs()) {
    result.replaceAllUsesWith(

				}
				originalRevision = out.(metav1.Object).GetResourceVersion()
			}

			// prepare result and pred
			pred := storage.SelectionPredicate{
				Label: tc.selector,
				Field: fields.Everything(),
				GetAttrs: func(obj runtime.Object) (labels.Set, fields.Set, error) {
					pod, ok := obj.(*examplev1.Pod)
					if !ok {
						return nil, nil, fmt.Errorf("not a pod")
					}
					return pod.ObjectMeta.Labels, fields.Set{

	}
	return storage.SelectionPredicate{
		Label:    labels.Everything().Add(*l),
		Field:    fields.Everything(),
		GetAttrs: getPodAttrs,
	}
}

func matchEverything() storage.SelectionPredicate {
	return storage.SelectionPredicate{
		Label: labels.Everything(),
		Field: fields.Everything(),
		GetAttrs: func(obj runtime.Object) (label labels.Set, field fields.Set, err error) {
			return nil, nil, nil
		},
	}
}

      OperationState new_state(op_with_region->getLoc(),
                               op_with_region->getName().getStringRef(), inputs,
                               output_types, op_with_region->getAttrs());
      for (int i = 0; i < op_with_region->getNumRegions(); ++i) {
        new_state.addRegion();
      }
      Operation* quantized_op = rewriter.create(new_state);
      for (const auto& [index, region] :

            builder.create<TF::TPUAnnotateTensorsWithDynamicShapeOp>(
                op->getLoc(), result.getType(), new_value,
                result.getDefiningOp()->getAttrs());
        for (auto [operation, index] : out_of_region_use) {
          if (!backward_pass_ops.contains(operation)) {
            operation->emitOpError()

	}
	for name, tc := range cases {
		t.Run(name, func(t *testing.T) {
			if tc.job == nil {
				_, _, err := GetAttrs(tc.nonJobObject)
				if diff := cmp.Diff(tc.wantErr, err.Error()); diff != "" {
					t.Errorf("Unexpected errors (-want,+got):\n%s", diff)
				}
			} else {
				gotLabels, _, err := GetAttrs(tc.job)
				if err != nil {
					t.Errorf("Error %s supposed to be nil", err.Error())
				}

        output_uniform_quantized_tensor_type};
    auto new_conv_op_with_output_type =
        rewriter.create<stablehlo::ConvolutionOp>(
            op.getLoc(), new_conv_output_types, op.getOperands(),
            op->getAttrs());

    rewriter.replaceAllUsesWith(op.getResult(),
                                new_conv_op_with_output_type.getResult());

    auto new_output_dequant_op =

  // type
  auto new_device_cluster = builder.create<mlir::tf_device::ClusterOp>(
      device_cluster.getLoc(), device_result_types,
      /*operands=*/llvm::ArrayRef<Value>{}, device_cluster->getAttrs());

  new_device_cluster.getBody().push_back(new Block);
  builder.setInsertionPointToEnd(&new_device_cluster.GetBody());

  // Create return op for device computation region in the paralle_execute op

    auto tf_fused_batch_norm_op_v3 = CreateTfOp<TF::FusedBatchNormV3Op>(
        rewriter, tf_fused_batch_norm_op, new_result_types,
        tf_fused_batch_norm_op.getOperands(),
        tf_fused_batch_norm_op->getAttrs());

    rewriter.replaceOp(tf_fused_batch_norm_op,
                       tf_fused_batch_norm_op_v3->getResults().drop_back());
    return success();
  }
};
}  // namespace.