// CHECK: return %[[RESULT]]

  %indices = "tf.Const"() {value = dense<[1, 0]> : tensor<2xi32>} : () -> tensor<2xi32>
  %0 = "tf.DynamicStitch"(%indices, %arg0) : (tensor<2xi32>, tensor<2x2xf32>) -> tensor<2x2xf32>
  func.return %0 : tensor<2x2xf32>
}

// CHECK-LABEL: DynamicStitch_scalar_matrix_indices

  %cst_0 = "tf.Const"() {value = dense<0.400000e+00> : tensor<2x2xf32>} : () -> tensor<2x2xf32>
  %0 = "tf.Conv2D"(%arg0, %cst) {data_format = "NHWC", dilations = [1, 1, 2, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<1x3x2x2xf32>
  %1 = "tf.Mul"(%0, %cst_0) : (tensor<1x3x2x2xf32>, tensor<2x2xf32>) -> tensor<1x3x2x2xf32>
  func.return %1 : tensor<1x3x2x2xf32>
}

  %0 = stablehlo.uniform_dequantize %arg : (tensor<2x2x!quant.uniform<i8:f32, 1.000000e+0:8>>) -> tensor<2x2xf32>
  return %0 : tensor<2x2xf32>
}
// CHECK-LABEL: uniform_dequantize_op
// CHECK: %[[DEQUANT:.+]] = "tfl.dequantize"({{.*}}) : (tensor<2x2x!quant.uniform<i8:f32, 1.000000e+00:8>>) -> tensor<2x2xf32>
// CHECK: return %[[DEQUANT]]

// -----

  %4 = "tf.MatMul"(%arg0, %3) {device = "", transpose_a = false, transpose_b = false} : (tensor<2x3xf32>, tensor<3x4xf32>) -> tensor<2x4xf32>
  %5 = "tf.Identity"(%4) {device = ""} : (tensor<2x4xf32>) -> tensor<2x4xf32>
  %6 = "tf.Identity"(%5) {device = ""} : (tensor<2x4xf32>) -> tensor<2x4xf32>
  func.return %6 : tensor<2x4xf32>

  // CHECK-LABEL: QuantDequantTranspose

// CHECK: [[UNPACK:%.*]]:3 = "tf.Unpack"(%arg0) <{axis = 0 : i64}> : (tensor<3x2x2xf32>) -> (tensor<2x2xf32>, tensor<2x2xf32>, tensor<2x2xf32>)
// CHECK: [[SCALAR_ZERO:%.*]] = arith.constant dense<0> : tensor<i32>
// CHECK: [[CONCAT:%.*]] = "tf.Concat"([[SCALAR_ZERO]], [[UNPACK]]#0, [[UNPACK]]#1, [[UNPACK]]#2) : (tensor<i32>, tensor<2x2xf32>, tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<?x2xf32>
// CHECK: [[LENGTHS:%.*]] = arith.constant dense<0> : tensor<0xi64>

// CHECK:           %[[VAL_6:.*]] = "tf.ArgMax"(%[[VAL_0]], %[[VAL_4]]) : (tensor<2x2x4xf32>, tensor<1xi32>) -> tensor<2x2xi32>
// CHECK:           return %[[VAL_5]], %[[VAL_6]] : tensor<2x2xf32>, tensor<2x2xi32>
// CHECK:         }
func.func @convert_argmax_constant(%arg0: tensor<2x2x4xf32>) -> (tensor<2x2xf32>, tensor<2x2xi32>) {

    %16 = "tf.Mean"(%15, %8) {keep_dims = false} : (tensor<2x112x112x64xf32>, tensor<2xi32>) -> tensor<2x64xf32>
    %17 = "tf.MatMul"(%16, %arg3) {transpose_a = false, transpose_b = false} : (tensor<2x64xf32>, tensor<64x1001xf32>) -> tensor<2x1001xf32>
    %18 = "tf.BiasAdd"(%17, %arg4) {data_format = "NHWC"} : (tensor<2x1001xf32>, tensor<1001xf32>) -> tensor<2x1001xf32>

          tensor<f32>, tensor<i32>) -> tensor<2x!tf_type.qint32>
    func.return %1 : tensor<2x!tf_type.qint32>
}

// -----

// CHECK-LABEL: func @while_region_qint
func.func @while_region_qint(%arg0: tensor<2x2xf32>) -> (tensor<2x?xf32>, tensor<?x2xf32>) {
  %scales = "tf.Const"() { value = dense<1.0> : tensor<f32> } : () -> tensor<f32>
  %zps2 = "tf.Const"() { value = dense<2> : tensor<i32> } : () -> tensor<i32>

// CHECK-NOT: quantfork.qcast
// CHECK-NOT: quantfork.dcast

// -----

// Test that q/dq pair is not inserted when constant has multiple users.

func.func @no_qdq_for_multiple_users(%arg0: tensor<2x2xf32>) -> tensor<2x3xf32> attributes {tf._original_func_name = "main_0"} {
  %cst = "tf.Const"() {value = dense<3.000000e-01> : tensor<2x3xf32>} : () -> tensor<2x3xf32>
  %0 = "tf.XlaCallModule"(%arg0, %cst) {

        %6 = "tf.TensorListSetItem"(%3, %4, %5) {device = ""} : (tensor<!tf_type.variant<tensor<*xf32>>>, tensor<i32>, tensor<2x2xf32>)-> tensor<*x!tf_type.variant>
        %7 = "tf.Const"() {device = "", value = dense<-1> : tensor<i32>} : () -> tensor<i32>