%2 = "tf.XlaCallModule"(%1#0, %0) <{Sout = [#tf_type.shape<1x3>], dim_args_spec = [], disabled_checks = [], has_token_input_output = false, module = "", platforms = [], version = 5 : i64}> {_entry_function = @composite_dot_general_fn_1, _original_entry_function = "composite_dot_general_fn_1", _quantization_method = "static_range_ptq {}", _stablehlo_module_attrs = {}, _tfl_quant_trait = "fully_quantizable", device = ""} : (tensor<1x1024xf32>,...

    "tf.InitializeTableFromTextFileV2"(%0, %arg0) {delimiter = "\09", device = "", key_index = -2 : i64, value_index = -1 : i64, vocab_size = 205 : i64} : (tensor<!tf_type.resource>, tensor<!tf_type.string>) -> ()
    func.return
  }

namespace tf {
namespace libtf {

namespace {

// AddTagged using tagged values
TaggedValue AddTagged(TaggedValue args, TaggedValue kwargs) {
  return TaggedValue(args.tuple()[0].i64() + args.tuple()[1].i64());
}

int64_t AddRaw(int64_t a, int64_t b) { return a + b; }

}  // namespace

// Add numbers in a loop by calling a callable.
void CallFunctions(::testing::benchmark::State& state) {

    %6 = "quantfork.qcast"(%5) {volatile} : (tensor<1x3xf32>) -> tensor<1x3x!quant.uniform<i8:f32, 1.000000e-03:-3>>

  %cpu = corert.get_op_handler %ch "cpu"
  %0 = corert.executeop(%cpu) "tf.Relu"(%arg0) { T = f32 } : 1
  %arg1 = tfrt_fallback_async.corert_tensorhandle_to_fallback_tensor %arg1_th {_tfrt_cost = 1 : i64, device = "/CPU:0"} : (!corert.tensorhandle) -> (!tfrt_fallback.tf_tensor)
  %1 = tfrt_fallback_async.executeop key(0) cost(100) device("/CPU:0") "tf.Relu"(%arg1) { T = f32 } : 1
  tfrt.return

      %2 = "quantfork.stats"(%1) {layerStats = dense<[5.00000000e-6, 7.00000000e-1]> : tensor<2xf32>} : (tensor<1x3xf32>) -> tensor<1x3xf32>
      return %2 : tensor<1x3xf32>

func (v TI16) String() string { return Sprintf("I16: %d", int16(v)) }
func (v TI32) String() string { return Sprintf("I32: %d", int32(v)) }
func (v TI64) String() string { return Sprintf("I64: %d", int64(v)) }
func (v TU) String() string   { return Sprintf("U: %d", uint(v)) }
func (v TU8) String() string  { return Sprintf("U8: %d", uint8(v)) }
func (v TU16) String() string { return Sprintf("U16: %d", uint16(v)) }

    return false;
  }
  if (AttrType content = mlir::dyn_cast<AttrType>(attr)) {
    *out_attr = content;
    return true;
  } else {
    return false;
  }
}

// Changes a DenseIntElementsAttr **containing I64** elements to an I32 Vector.
bool DenseI64AttrToI32Vector(const DenseIntElementsAttr& dense_attr,
                             std::vector<int32_t>* out_vec);

// Gets boolean from composite attrs if it exists.

func.func @fake_quant_with_min_max_vars(%arg0: tensor<1x1x28x48xf32>, %arg1: tensor<f32>, %arg2: tensor<f32>) -> tensor<1x1x28x48xf32> {
  %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2) {device = "", narrow_range = true, num_bits = 8 : i64} : (tensor<1x1x28x48xf32>, tensor<f32>, tensor<f32>) -> tensor<1x1x28x48xf32>
  func.return %0 : tensor<1x1x28x48xf32>
  // CHECK-SKIP: tf.FakeQuantWithMinMaxVars
  // CHECK-NOSKIP-NOT: tf.

type ErrorTest struct {
	name string
	fn   func()
	err  string
}

var (
	i, j, k       int   = 0, 0, 1
	i8, j8, k8    int8  = 0, 0, 1
	i16, j16, k16 int16 = 0, 0, 1
	i32, j32, k32 int32 = 0, 0, 1
	i64, j64, k64 int64 = 0, 0, 1

	bb = []int16{2, 0}

	u, v, w       uint    = 0, 0, 1
	u8, v8, w8    uint8   = 0, 0, 1
	u16, v16, w16 uint16  = 0, 0, 1
	u32, v32, w32 uint32  = 0, 0, 1
	u64, v64, w64 uint64  = 0, 0, 1