pm.addNestedPass<mlir::func::FuncOp>(
      mlir::quant::stablehlo::CreateConvertTFQuantOpsToMHLOPass());
  pm.addPass(mlir::createCanonicalizerPass());

  // TF -> StableHLO legalization.
  // Skip StatefulPartitionedCall to preserve aliased functions.
  mlir::odml::AddLegalizeTFToStablehloPasses(pm, /*skip_quantization_ops=*/true,
                                             /*skip_resize=*/false,

  // CHECK: return %[[SUBGRAPH_1]] : tensor<1024x3xf32>
  // CHECK: }
}

// -----

// main function contains PartitionedCall and StatefulPartitionedCall ops which
// is used to preserve aliased functions. This test make sure stablehlo ops in
// each PartitionedCall functions are lifted.

  // we don't have any evidence that choosing a stateless partitioned call helps
  // for performance.
  ops::StatefulPartitionedCall call(
      root.WithOpName("stateful_partitioned_call"), args, n->output_types(),
      func, ops::StatefulPartitionedCall::Attrs{}.ConfigProto(config_string));

  for (const Edge* e : n->in_edges()) {
    if (e->IsControlEdge()) {

  func.func @f(%arg0: tensor<!tf_type.resource<tensor<f32>>> {tf_saved_model.bound_input = @v}) -> (tensor<f32> {tf_saved_model.index_path = []})
  attributes {tf_saved_model.exported_names = ["f"]} {
    %val = "tf.StatefulPartitionedCall"(%arg0) {config = "", config_proto = "", executor_type = "", f = @f_callee} : (tensor<!tf_type.resource<tensor<f32>>>) -> (tensor<f32>)
    func.return %val : tensor<f32>
  }

//         dtype: DT_FLOAT
//         tensor_shape: {
//         }
//       }
//     }
//     outputs: {
//       key  : "output_0"
//       value: {
//         name : "StatefulPartitionedCall:0"
//         dtype: DT_FLOAT
//         tensor_shape: {
//         }
//       }
//     }
//     method_name: "tensorflow/serving/predict"
//   }
// }

  }
}

TF::StatefulPartitionedCallOp MakeFuncCaller(
    mlir::OpBuilder& builder, const Location& loc, func::FuncOp func,
    const llvm::SetVector<Value>& operands) {
  // Constructs a tf.StatefulPartitionedCall to the function provided in 'func'
  // using the operands in 'operands'. Assumes the insertion point on builder is
  // already set.
  auto symbol =

  // CHECK: [[results_0:%.*]]:2 = tfrt.call @callee([[chain]]
  // CHECK-SAME: (!tfrt.chain, !tfrt_fallback.tf_tensor) -> (!tfrt.chain, !tfrt_fallback.tf_tensor)
  %1 = "tf.StatefulPartitionedCall"(%0) {config = "", config_proto = "", executor_type = "", f = @callee} : (tensor<i32>) -> (tensor<i32>)
  // CHECK-NEXT: [[results_1:%.*]]:2 = tfrt.call @callee([[chain]]

  func.func @serving_default(%arg0: tensor<8xf32> {tf_saved_model.index_path = ["input"]}) -> (tensor<8xf32> {tf_saved_model.index_path = ["output"]})
    attributes {tf.entry_function = {control_outputs = "", inputs = "", outputs = "StatefulPartitionedCall:0"}, tf_saved_model.exported_names = ["serving_default"]} {
    %cst_0 = "tf.Const"() {device = "", value = dense<1.0> : tensor<8xf32>} : () -> tensor<8xf32>

    return llvm::isa_and_nonnull<mlir::TF::StatefulPartitionedCallOp>(op)
               ? mlir::WalkResult::interrupt()
               : mlir::WalkResult::advance();
  });
  if (result.wasInterrupted()) {
    // StatefulPartitionedCall ops are not supported by the tflite runtime.
    mlir::TFL::AttachErrorCode(
        module.emitError(
            "The Graph contains unsupported `StatefulPartionedCallOp`(s), will "

  // TPUCompilationResultsOp.
  if (op->hasAttr(kTpuCompilationStatus)) {
    op->setAttr(kTpuCompilationStatus, attr);
  }
}

// A helper class to inline TF::StatefulPartitionedCall ops
struct Inliner : public InlinerInterface {
  Inliner(OpBuilder& builder, SymbolTable& symbol_table)
      : InlinerInterface(builder.getContext()),
        builder(builder),