TEST_F(PjRtExecutionUtilGpuTest, PreparePjRtExecutableArguments) {
  std::vector<const Tensor*> inputs;
  inputs.push_back(CreateDeviceTensor<int32_t>(TensorShape({1, 3}), {0, 0, 0}));
  inputs.push_back(CreateDeviceTensor<int32_t>(TensorShape({1, 3}), {1, 2, 3}));
  inputs.push_back(CreateDeviceTensor<int32_t>(TensorShape({1, 3}), {4, 5, 6}));
  int num_missing_prefix_ctx_inputs = 2;
  std::vector<int> input_mapping{3, 4};
  std::vector<VariableInfo> variables;

#include "xla/client/client_library.h"
#include "xla/shape.h"
#include "xla/stream_executor/platform.h"
#include "xla/stream_executor/platform_manager.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/protobuf/config.pb.h"
#include "tensorflow/core/protobuf/tpu/compile_metadata.pb.h"
#include "tensorflow/core/tpu/kernels/tpu_compile_op_support.h"

#include "tensorflow/c/eager/c_api_unified_experimental_internal.h"
#include "tensorflow/c/tf_status.h"
#include "tensorflow/c/tf_status_helper.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/lib/llvm_rtti/llvm_rtti.h"
#include "tensorflow/core/platform/errors.h"

namespace tensorflow {

AbstractContext* BuildFunction(const char* fn_name) {

#include "tensorflow/core/common_runtime/shape_refiner.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/graph/graph.h"
#include "tensorflow/core/graph/node_builder.h"
#include "tensorflow/core/platform/mutex.h"
#include "tensorflow/core/platform/status.h"

#include "tensorflow/c/eager/c_api_unified_experimental_internal.h"
#include "tensorflow/c/eager/unified_api_testutil.h"
#include "tensorflow/c/tf_status_helper.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/lib/llvm_rtti/llvm_rtti.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/test.h"

namespace tensorflow {
namespace {
class UnifiedAPI

GetShapeDeterminationFns() {
  XlaHelpers::ShapeRepresentationFn shape_representation_fn =
      [](const TensorShape&, DataType, bool, XlaLayoutPreference) {
        return xla::Shape();
      };
  XlaShapeLayoutHelpers::LayoutPreferenceFn layout_preference_fn =
      [](const TensorShape&, DataType, std::optional<XlaArgument::Kind>) {
        return tensorflow::XlaLayoutPreference::kTpuPreferLinearLayout;
      };

#include <optional>
#include <string>

#include "absl/container/flat_hash_set.h"
#include "llvm/ADT/MapVector.h"
#include "llvm/ADT/StringMap.h"
#include "tensorflow/core/framework/tensor_shape.pb.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/lib/core/status.h"

namespace tensorflow {

struct ArrayInfoBase {

namespace {
absl::StatusOr<bool> IsConstantSmall(Node* n) {
  const TensorProto* proto = nullptr;
  TF_RETURN_IF_ERROR(GetNodeAttr(n->def(), "value", &proto));

  int64_t total_elements = 1;
  for (const auto& dim : proto->tensor_shape().dim()) {
    if (dim.size() < 0) {
      return errors::Internal("Unknown dimension size in constant tensor ",
                              n->name());
    }
    total_elements *= dim.size();
  }

  std::unique_ptr<Graph> graph(new Graph(OpRegistry::Global()));
  TF_CHECK_OK(root.ToGraph(graph.get()));

  std::map<int, InferredShape> arg_shapes;
  arg_shapes[0].shape = TensorShape({2, 3});
  arg_shapes[1].shape = TensorShape({2, 3});

  GraphShapeInfo shape_info;
  TF_ASSERT_OK(InferShapes(graph.get(), arg_shapes,
                           /*fnlib_def=*/nullptr, &shape_info));

#include "tensorflow/compiler/mlir/tensorflow/translate/tf_mlir_translate.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/convert_type.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/platform/status.h"
#include "tsl/platform/errors.h"
#include "tfrt/bef_converter/mlir_to_bef.h"  // from @tf_runtime

namespace tensorflow {