// 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),

      result.replaceAllUsesWith(if_op.getOperand(then_aliased_input + 1));
    }
  }
  if_op.replaceAllUsesWith(new_if);
  if_op.erase();
  return success();
}

// Handles stack usage by a tf.StatefulPartitionedCall or a tf.PartitionedCall.
// It will first check if the callee was previously handled, and try to reuse
// that result if so. Otherwise, it will clone and convert the callee function,

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

  // expected-remark@above {{ID: 0}}
  %r1 = "tf.ReadVariableOp"(%arg0) : (tensor<!tf_type.resource>) -> tensor<1xf32>
  // expected-remark@above {{ID: 1}}
  %two = "tf.StatefulPartitionedCall"(%one, %one) {config="", config_proto="", executor_type="", f=@add} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
  // expected-remark@above {{ID: 2}}

                                                arg_buffer_size_is_fixed(i)};
    }
  }
  UpdateFuncType(func);
}

// Holds information about a decomposed callee function for
// PartitionedCall/StatefulPartitionedCall.
struct PartitionedCallDecompositionInfo {
  bool signature_change;
  func::FuncOp decomposed_callee;
  llvm::SmallDenseMap<int64_t, int64_t> buffer_arg_to_size_arg;

    // (checked later).
    if (auto device_type_attr =
            op.getAttrOfType<StringAttr>(mlir::TF::kCompileDeviceTypeAttr)) {
      // tf.StatefulPartitionedCall ops with and without
      // _tpu_replicate attributes may exist in the same graph. Ops without
      // the attribute but with _XlaMustCompile=true would have
      // _xla_compile_device_type="" after

    %0 = "tf.PartitionedCall"(%arg0) {config = "", config_proto = "", executor_type = "", f = @partitioned_called_func} : (tensor<20xi32>) -> tensor<*xi32>
    // CHECK: tf.StatefulPartitionedCall
    // CHECK-SAME: (tensor<20xi32>) -> tensor<20xi32>
    %1 = "tf.StatefulPartitionedCall"(%arg0) {config = "", config_proto = "", executor_type = "", f = @stateful_partitioned_call_func} : (tensor<20xi32>) -> tensor<*xi32>
    // CHECK: tf.TPUPartitionedCall