%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>

	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)

// PerTensorWeightOnly-LABEL: QuantizeConv2D
// BLOCK-LABEL: QuantizeConv2D
func.func @QuantizeConv2D(%arg0: tensor<1x224x224x3xf32>) -> tensor<1x112x112x64xf32> {
  %w = arith.constant dense<1.270000e+02> : tensor<64x3x3x3xf32>
  %b = arith.constant dense<-1.23697901> : tensor<64xf32>

func.func @testConcatOffsetWithZeros() -> (tensor<3xi32>, tensor<3xi32>, tensor<3xi32>, tensor<3xi32>) {
  %concat_dim = arith.constant dense<1> : tensor<i32>
  %shape0 = arith.constant dense<0> : tensor<3xi32>
  %shape1 = arith.constant dense<[0, 3, 0]> : tensor<3xi32>
  %shape2 = arith.constant dense<[0, 5, 0]> : tensor<3xi32>
  %shape3 = arith.constant dense<0> : tensor<3xi32>

  // CHECK-DAG: [[OFFSET_0:%.*]] = "tf.Const{{.*}} dense<0> : tensor<3xi32>

  %0 = "quantfork.stats"(%arg0) {layerStats = dense<[0.000000e+00, 2.550000e+02]> : tensor<2xf32>} : (tensor<1x1x1x1xf32>) -> tensor<1x1x1x1xf32>
  %w_1 = arith.constant dense<127.0> : tensor<4096x1x1x1xf32>
  %w_2 = arith.constant dense<127.0> : tensor<128x1x1x1xf32>
  %b = arith.constant dense<127.0> : tensor<2048x1x1x1xf32>

func.func @testDilatedConvWithPad(%arg0: tensor<1x128x128x3xf32>, %arg1: tensor<5x5x3x8xf32>, %arg2: tensor<8xf32>) -> tensor<1x128x128x8xf32> {
  %cst = arith.constant dense<[2, 2]> : tensor<2xi32>
  %cst_0 = arith.constant dense<4> : tensor<2x2xi32>
  %cst_1 = arith.constant dense<0> : tensor<2x2xi32>
  %cst_2 = arith.constant dense<0> : tensor<4x2xi32>

// This transformation pass applies quantization propagation on TF dialect.

#include <algorithm>
#include <memory>
#include <string>
#include <utility>
#include <vector>

#include "mlir/Dialect/Arith/IR/Arith.h"  // from @llvm-project
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/Dialect/Quant/QuantOps.h"  // from @llvm-project
#include "mlir/Dialect/Quant/QuantTypes.h"  // from @llvm-project

  %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
  %res = scf.if %eq -> !tfr.tensor {
    scf.yield %v1 : !tfr.tensor
  } else {
    %step = arith.index_cast %cst : i32 to index
    %end = arith.index_cast %n : i32 to index

// CHECK:           %[[VAL_1:.*]] = arith.constant dense<[0, 2, 3, 1]> : tensor<4xi32>
// CHECK:           %[[VAL_2:.*]] = "tfl.transpose"(%[[VAL_0]], %[[VAL_1]]) : (tensor<1x3x6x6xf32>, tensor<4xi32>) -> tensor<1x6x6x3xf32>
// CHECK:           %[[VAL_3:.*]] = arith.constant dense<0> : tensor<4x2xi32>

class PrepareDynamicRangeQuantizableOp
    : public OpRewritePattern<arith::ConstantOp> {
 public:
  explicit PrepareDynamicRangeQuantizableOp(
      MLIRContext* context, const quant::QuantizationSpecs& quant_specs,
      llvm::SetVector<Operation*>* const visited_nonquantizable_ops)
      : OpRewritePattern<arith::ConstantOp>(context),
        visited_nonquantizable_ops_(visited_nonquantizable_ops),