%2 = "tf.Relu6"(%1) {device = ""} : (tensor<1x2x2x2xf32>) -> tensor<1x2x2x2xf32>
  return %2 : tensor<1x2x2x2xf32>
}
// CHECK-LABEL: @composite_conv2d_with_bias_and_relu6_fn_1
// CHECK-NOT: "tf.CalibrationStatisticsSaver"

// -----

// Check the IfOp is set to stateful.

  %dequant = stablehlo.uniform_dequantize %requant : (tensor<1x3x4x2x!quant.uniform<i8:f32, 0.045673100153605144:-62>>) -> tensor<1x3x4x2xf32>
  func.return %dequant : tensor<1x3x4x2xf32>
}

// -----

// CHECK-LABEL: @dont_merge_quantization_followed_by_quantization
// CHECK-SAME: %[[ARG_0:.*]]: tensor<1x3x4x3xf32>

  %r = "mhlo.concatenate"(%0, %1, %2) <{dimension = 0 : i64}> : (tensor<1x1x512xf32>, tensor<1x1x512xf32>, tensor<1x1x512xf32>) -> tensor<3x1x512xf32>
  func.return %r : tensor<3x1x512xf32>

// CHECK: return %arg0 : tensor<3x1x512xf32>
}

// -----

// CHECK-LABEL: testConvertReshapeDotRhsToBatchedDot

// CHECK:  [[VAL_3:%.*]] = "tfl.reshape"([[VAL_2]], %{{.*}}) : (tensor<1x1x1x2xf32>, tensor<2xi32>) -> tensor<2x1xf32>

// RUN: tf-quant-opt %s -split-input-file -quant-propagate-quantize-type | FileCheck %s

module {
  func.func @not_propagate_matmul(%arg0: tensor<1x2x2x2xf32>) -> tensor<*xf32> {
    %cst = "tf.Const"() {value = dense<127> : tensor<2x1024xi8>} : () -> tensor<2x1024xi8>
    %cst_0 = "tf.Const"() {value = dense<0.0157480314> : tensor<f32>} : () -> tensor<f32>
    %0 = "tf.Identity"(%cst) : (tensor<2x1024xi8>) -> tensor<2x1024xi8>

  %cst2 = arith.constant dense<[1.0, 2.0]> : tensor<2xf32>
  %0 = "tf.Conv2D"(%arg0, %cst0) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", dilations = [1, 2, 3, 1], padding = "SAME", strides = [1, 4, 5, 1]} : (tensor<1x112x112x3xf32>, tensor<1x3x3x2xf32>) -> tensor<1x28x23x2xf32>

  func.return %result : tensor<1x1x512xf32>

  // CHECK:  %[[MUL3:.*]] = "tf.Mul"(%[[MEAN]], %[[MUL1]]) : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xf32>
  // CHECK:  %[[SUB:.*]] = "tf.Sub"(%arg2, %[[MUL3]]) : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xf32>
  // CHECK:  %[[ADD0:.*]] = "tf.Add"(%[[MUL2]], %[[SUB]]) : (tensor<1x1x6x2xf32>, tensor<2xf32>) -> tensor<1x1x6x2xf32>

func.func @test_gather(%arg0 : tensor<1x2x20xf32>, %arg1 : tensor<3x5xi32>) -> (tensor<1x3x5x20xf32>){
    // expected-remark@+1 {{Cost: 1}}
    %0 = "tf.Const"() { value = dense<[1]> : tensor<1xi32> } : () -> tensor<1xi32>
    // expected-remark@+1 {{Cost: 300}}
    %1 = "tf.GatherV2"(%arg0, %arg1, %0) : (tensor<1x2x20xf32>, tensor<3x5xi32>, tensor<1xi32>) -> tensor<1x3x5x20xf32>
    // expected-remark@+1 {{Cost: 40}}
    func.return %1 : tensor<1x3x5x20xf32>

    %8 = "tf.Cast"(%5) {Truncate = false} : (tensor<1x3x2x2xi32>) -> tensor<1x3x2x2xf32>
    %9 = "tf.Mul"(%7, %8) : (tensor<f32>, tensor<1x3x2x2xf32>) -> tensor<1x3x2x2xf32>
    %10 = "tf.Round"(%9) : (tensor<1x3x2x2xf32>) -> tensor<1x3x2x2xf32>
    %11 = "tf.Cast"(%10) {Truncate = false} : (tensor<1x3x2x2xf32>) -> tensor<1x3x2x2xi32>
    %12 = "tf.AddV2"(%11, %arg10) : (tensor<1x3x2x2xi32>, tensor<i32>) -> tensor<1x3x2x2xi32>