// CHECK: return %[[DEQUANTIZED]] : tensor<*xf32>
}

// -----

module {
  func.func @not_quantize_matmul_without_const(%arg0: tensor<1x2x2x2xf32>, %arg1: tensor<2x1024xf32>) -> (tensor<*xf32>) {
    %arg0_identity = "tf.Identity"(%arg0) {device = ""} : (tensor<1x2x2x2xf32>) -> tensor<1x2x2x2xf32>
    %arg1_identity = "tf.Identity"(%arg1) {device = ""} : (tensor<2x1024xf32>) -> tensor<2x1024xf32>

  %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.

  %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

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>

  func.return %mm_s : tensor<1x3x3x2xf32>

// CHECK: %[[w:.*]] = arith.constant dense<1.270000e+02> : tensor<512x2xf32>
// CHECK: %[[q_w:.*]] = "tfl.quantize"(%[[w]]) <{qtype = tensor<512x2x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>}>
// CHECK: %[[dq_w:.*]] = "tfl.dequantize"(%[[q_w]]) : (tensor<512x2x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>) -> tensor<512x2xf32>

  %21 = "tf.Const"() {device = "", name = "Const_143", dtype = "tfdtype$DT_FLOAT", value = dense<0.24288677062973696> : tensor<1x1x6x2xf32>} : () -> tensor<1x1x6x2xf32>
  // CHECK-DAG: value = #tf_type<tensor_proto
  // CHECK-DAG: tf.Const{{.*}} dense<0.242886767> : tensor<1x1x6x2xf32>
  func.return %0, %21 : tensor<4xf32>, tensor<1x1x6x2xf32>
}

// CHECK-LABEL: func @testAdd() -> tensor<2x2xi32>
func.func @testAdd() -> tensor<2x2xi32> {
^bb0:

// CHECK:           %[[VAL_7:.*]] = "tfl.concatenation"(%[[VAL_5]], %[[VAL_6]]) <{axis = 3 : i32, fused_activation_function = "NONE"}> : (tensor<1x1x1x1xf32>, tensor<1x1x1x1xf32>) -> tensor<1x1x1x2xf32>
// CHECK:           %[[VAL_8:.*]] = "tfl.reshape"(%[[VAL_7]], %[[VAL_3]]) : (tensor<1x1x1x2xf32>, tensor<1xi32>) -> tensor<2xf32>
// CHECK:           %[[VAL_9:.*]] = "tfl.reshape"(%[[VAL_8]], %[[VAL_4]]) : (tensor<2xf32>, tensor<2xi32>) -> tensor<2x1xf32>

    %3 = "tfl.reshape"(%2, %cst_0) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<1x1x1x2xf32>, tensor<1xi32>) -> tensor<2xf32>
    %4 = "tfl.reshape"(%3, %cst_1) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<2xf32>, tensor<2xi32>) -> tensor<2x1xf32>

// CHECK:           %[[VAL_8:.*]] = "tfl.reshape"(%[[VAL_7]], %[[VAL_3]]) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<1x1x1x2xf32>, tensor<1xi32>) -> tensor<2xf32>