func.return %3 : tensor<64xf32>

// CHECK-LABEL: func @reshape_removeAdjacentWithMultipleUse
// CHECK:  %[[CST:.*]] = arith.constant dense<64> : tensor<1xi32>
// CHECK:  %[[RESHAPE_1:.*]] = "tfl.reshape"(%arg0, %[[CST]]) : (tensor<4x4x4xf32>, tensor<1xi32>) -> tensor<64xf32>
// CHECK:  %[[RESHAPE_2:.*]]  = "tfl.reshape"(%arg0, %[[CST]]) : (tensor<4x4x4xf32>, tensor<1xi32>) -> tensor<64xf32>

  %index = arith.constant 0 : index
  %cst = arith.constant 1 : i32
  %eq = arith.cmpi eq, %n, %cst : i32
  %v1 = tfr.get_element %values[%index] : (!tfr.tensor_list, index) -> !tfr.tensor
  %temp = tfr.call @tf__expand_dims(%v1, %axis) : (!tfr.tensor, i32) -> !tfr.tensor
  %res = scf.if %eq -> !tfr.tensor {
    scf.yield %temp : !tfr.tensor
  } else {
    %step = arith.index_cast %cst : i32 to index

    %cst_3 = arith.constant dense<1.0> : tensor<20x20xf32>
    %cst_7 = arith.constant dense<1.0> : tensor<20xf32>
    %recurrent_input = arith.constant dense<1.0> : tensor<1x20xf32>
    %recurrent_stats = "quantfork.stats"(%recurrent_input) {layerStats = dense<[-2.0, 1.0]> : tensor<2xf32>} : (tensor<1x20xf32>) -> tensor<1x20xf32>
    %cell_input = arith.constant dense<1.0> : tensor<1x20xf32>

// CHECK-LABEL: @fuseMulIntoConv2d
func.func @fuseMulIntoConv2d(%arg0: tensor<1x112x112x3xf32>) -> tensor<1x28x23x2xf32> {
  %cst0 = arith.constant dense<[[[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], [[7.0, 8.0], [9.0, 10.0], [11.0, 12.0]], [[13.0, 14.0], [15.0, 16.0], [17.0, 18.0]]]]> : tensor<1x3x3x2xf32>
  %cst2 = arith.constant dense<[1.0, 2.0]> : tensor<2xf32>

  %weight = arith.constant dense<1.0> : tensor<2x3x3x2xf32>
  %bias = arith.constant dense<[7.11401462, 7.05456924]> : tensor<2xf32>
  %cst = arith.constant dense<[-1, 144]> : tensor<2xi32>

    rewriter.replaceOpWithNewOp<arith::ConstantOp>(op, op.getValue());
    return success();
  }
};

class ConvertArithConstToStablehloConstOp
    : public OpRewritePattern<arith::ConstantOp> {
 public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(arith::ConstantOp op,
                                PatternRewriter& rewriter) const override {

// CHECK-LABEL: fuseScalarAddIntoConv2dHalf
func.func @fuseScalarAddIntoConv2dHalf(%arg0: tensor<256x32x32x3xf16>, %arg1: tensor<16x3x3x3xf16>) -> tensor<256x8x7x16xf16> {
  %cst = arith.constant dense<1.5> : tensor<f16>
  %cst_0 = arith.constant dense<[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0]> : tensor<16xf16>

// CHECK:  return %1
}

// CHECK-LABEL: fakeQuantFolded
func.func @fakeQuantFolded() -> (tensor<8xf32>) {
  %in = arith.constant dense<0.0> : tensor<8xf32>
  %min = arith.constant dense<0.0> : tensor<f32>
  %max = arith.constant dense<15.0> : tensor<f32>
  %mini = "tf.Identity"(%min) : (tensor<f32>) -> tensor<f32>
  %maxi = "tf.Identity"(%max) : (tensor<f32>) -> tensor<f32>

	httpOnce.Do(startHttpServer)
}

func startNewServer() {
	newServer = NewServer()
	newServer.Register(new(Arith))
	newServer.Register(new(Embed))
	newServer.RegisterName("net.rpc.Arith", new(Arith))
	newServer.RegisterName("newServer.Arith", new(Arith))

	var l net.Listener
	l, newServerAddr = listenTCP()
	log.Println("NewServer test RPC server listening on", newServerAddr)
	go newServer.Accept(l)

  func.return %0 : tensor<2x1x7x8x1xf32>

  // CHECK-DAG: %[[BEGIN:.*]] = arith.constant dense<0> : tensor<5xi32>
  // CHECK-DAG: %[[END:.*]] = arith.constant dense<[2, 3, 0, 0, 5]> : tensor<5xi32>
  // CHECK-DAG: %[[STEP:.*]] = arith.constant dense<1> : tensor<5xi32>
  // CHECK-DAG: %[[NEW_DIMS:.*]] = arith.constant dense<[6, 1, 7, 8, 1]> : tensor<5xi32>