// 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>

// 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>

#include "tensorflow/compiler/mlir/lite/utils/constant_utils.h"

#include <complex>
#include <cstdint>
#include <string>
#include <vector>

#include "absl/status/status.h"
#include "mlir/Dialect/Arith/IR/Arith.h"  // from @llvm-project
#include "mlir/IR/Attributes.h"  // from @llvm-project
#include "mlir/IR/BuiltinAttributeInterfaces.h"  // from @llvm-project
#include "mlir/IR/BuiltinAttributes.h"  // from @llvm-project

  %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>

// -----

// Test for case with import and find ops.
func.func @hashtable_import_then_find(%arg0: tensor<5x!tf_type.string>) -> tensor<*xi64> {
  %cst = arith.constant dense<["emerson", "lake", "palmer"]> : tensor<3x!tf_type.string>
  %cst_0 = arith.constant dense<-1> : tensor<i64>
  %cst_1 = arith.constant dense<[0, 1, 2]> : tensor<3xi64>