// CHECK: %[[qsf:.*]] = tfr.quant_scale_factor(%[[cst_0]], %[[list]]) : (f32, !tfr.tensor_list) -> !tfr.tensor
// CHECK: return %[[qsf]] : !tfr.tensor
}

// CHECK-LABEL: decompose_quant_rescale
func.func @decompose_quant_rescale(%arg0: tensor<2xi32>) -> !tfr.tensor {
  %zp = arith.constant 67 : i64
  %cst = "tf.Const"() {value = dense<1.0> : tensor<f32>} : () -> tensor<f32>
  %scale_factor = "tfr.cast"(%cst) : (tensor<f32>) -> !tfr.tensor

func.func @qdq_for_conv_weight_empty(%arg0: tensor<1x3x2x3xf32>) -> tensor<1x2x2x2xf32> attributes {tf._original_func_name = "main_0"} {
  %cst = "tf.Const"() {value = dense<3.000000e-01> : tensor<2x3x3x2xf32>} : () -> tensor<2x3x3x2xf32>
  %0 = "tf.XlaCallModule"(%arg0, %cst) {
    Sout = [#tf_type.shape<1x2x2x2>], _entry_function = @composite_conv_fn,
    _original_entry_function = "composite_conv_fn",

      //
      %1 = "tf.XlaReduceScatter"(%arg0, %cst_0, %cst) {reduce_op = "Add"} : (tensor<128x128xf32>, tensor<4x2xi32>, tensor<i32>) -> tensor<64x128xf32>
      func.return %1 : tensor<64x128xf32>
  }

  // CHECK-LABEL: func @tf_mod
  func.func @tf_mod(%arg1: tensor<2x2xf32>) -> tensor<2x2xf32> {
    %cst = "tf.Const"() {value = dense<7.000000e+00> : tensor<f32>} : () -> tensor<f32>

	continuec := make(chan struct{})
	cst := newClientServerTest(t, mode, HandlerFunc(func(w ResponseWriter, r *Request) {
		ctl := NewResponseController(w)
		w.Write([]byte("one"))
		if err := ctl.Flush(); err != nil {
			t.Errorf("ctl.Flush() = %v, want nil", err)
			return
		}
		<-continuec
		w.Write([]byte("two"))
	}))

	res, err := cst.c.Get(cst.ts.URL)
	if err != nil {

        %cst = "tf.Const"() {device = "", value = dense<[1.0, 2.0]> : tensor<2xf32>} : () -> tensor<2xf32>
        %0 = "tf.VarHandleOp"() {container = "", device = "/device:CPU:0", shared_name = "var_0"} : () -> tensor<!tf_type.resource<tensor<2xf32>>>
        "tf.AssignVariableOp"(%0, %cst) : (tensor<!tf_type.resource<tensor<2xf32>>>, tensor<2xf32>) -> ()

  ElementsAttr attr;
  ShapedType type;
  InspectResult result = {};
  if (auto cst = dyn_cast<ConstOp>(inst)) {
    attr = cst.getValue();
    type = mlir::cast<ShapedType>(cst.getType());
  } else if (auto cst = dyn_cast<QConstOp>(inst)) {
    attr = cst.getValue();
    type = mlir::cast<ShapedType>(cst.getType());
  } else {
    result.can_compress = false;
    return result;
  }

func.func @preparePrelu(%arg0: tensor<1x10x10x3xf32>) -> tensor<1x10x10x3xf32> {
  %cst = "arith.constant"() {value = dense<[[[1.66394591, 3.61694336, 2.0382936]]]> : tensor<1x1x3xf32>} : () -> tensor<1x1x3xf32>
  %prelu = "tfl.prelu"(%arg0, %cst) : (tensor<1x10x10x3xf32>, tensor<1x1x3xf32>) -> tensor<1x10x10x3xf32>
  func.return %prelu : tensor<1x10x10x3xf32>

// LSTMOpQuantized: %[[lstm:.*]] = "tfl.unidirectional_sequence_lstm"(%arg0, %[[dq1]], %[[dq1]], %[[dq1]], %[[dq1]], %[[dq1]], %[[dq1]], %[[dq1]], %[[dq1]], %[[dq3]], %[[dq3]], %[[dq3]], %cst_0, %cst_0, %cst_0, %cst_0, %[[dq1]], %0, %cst_1, %cst_1, %0, %0, %0, %0)

// LSTMOpNotQuantized-DAG: %[[cst_1:.*]] = arith.constant dense<1.000000e+00> : tensor<20x20xf32>

func.func @testWhileRegionExternInBodyAndCond(%arg0 : tensor<*xf32>, %arg1 : tensor<i32>, %arg2 : tensor<i32>) -> tensor<*xf32> {
  %cst = arith.constant dense<4> : tensor<i32>
  %stride = "tf.Add"(%arg2, %cst) : (tensor<i32>, tensor<i32>) -> tensor<i32>
  %cst1 = arith.constant dense<44> : tensor<i32>
  %limit = "tf.Add"(%arg2, %cst1) : (tensor<i32>, tensor<i32>) -> tensor<i32>

                public void visitLdcInsn(Object cst) {
                    if (cst instanceof String) {
                        String literal = remappedClassLiterals.get(cst);
                        if (literal == null) {
                            // tries to relocate literals in the form of foo/bar/Bar
                            literal = remapper.maybeRelocateResource((String) cst);
                        }