// CHECK-LABEL: roll_non_constant_shift
  // CHECK:  "tf.Roll"
}

func.func @scatter_nd_updates(%arg0: tensor<14xf32>, %arg1: tensor<1x1xi32>, %arg2: tensor<1xf32>) -> tensor<14xf32> {
  %0 = "tf.TensorScatterUpdate"(%arg0, %arg1, %arg2) : (tensor<14xf32>, tensor<1x1xi32>, tensor<1xf32>) -> tensor<14xf32>
  func.return %0 : tensor<14xf32>

  // CHECK-LABEL: scatter_nd_updates

    %0 = "tf.Const"() {value = dense<1> : tensor<i64>} : () -> tensor<i64>
    %1 = "tf.Const"() {value = dense<0> : tensor<1x1xi32>} : () -> tensor<1x1xi32>
    %2 = "tf.Const"() {value = dense<[7, 7, 3, 64]> : tensor<4xi32>} : () -> tensor<4xi32>
    %3 = "tf.Const"() {value = dense<[[0, 0], [3, 3], [3, 3], [0, 0]]> : tensor<4x2xi32>} : () -> tensor<4x2xi32>

func.func @scatter_nd_add(%arg0: tensor<7xi64>, %arg1: tensor<1x1xi32>, %arg2: tensor<1xi64>) -> tensor<7xi64> {
  %0 = "tf.TensorScatterAdd"(%arg0, %arg1, %arg2) : (tensor<7xi64>, tensor<1x1xi32>, tensor<1xi64>) -> tensor<7xi64>
  func.return %0 : tensor<7xi64>

  // CHECK-LABEL: scatter_nd_add
  // CHECK:  %[[GATHER:.*]] = "tf.GatherNd"(%arg0, %arg1) <{bad_indices_policy = ""}> : (tensor<7xi64>, tensor<1x1xi32>) -> tensor<1xi64>

}

func.func @DontConvertIdentityScatterNdWithLargerOutputDimSize(%arg0: tensor<1xf32>) -> tensor<2xf32> {
  %cst = arith.constant dense<[[0]]> : tensor<1x1xi32>
  %shape = arith.constant dense<[2]> : tensor<1xi32>
  %0 = "tfl.scatter_nd"(%cst, %arg0, %shape) : (tensor<1x1xi32>, tensor<1xf32>, tensor<1xi32>) -> tensor<2xf32>
  func.return %0 : tensor<2xf32>

// CHECK-LABEL: DontConvertIdentityScatterNdWithLargerOutputDimSize

  %0 = "tf.ConcatV2"(%arg, %arg, %axis) : (tensor<8x16xf32>, tensor<8x16xf32>, tensor<1x1xi32>) -> tensor<*xf32>
  func.return %0 : tensor<*xf32>
}

// -----

func.func @testConcatV2(%arg: tensor<8x16xf32>, %axis: tensor<1x1xi32>) -> tensor<*xf32> {

  %0 = stablehlo.constant() {value = dense<1> : tensor<1x3x2xi8>} : () -> tensor<1x3x2x!quant.uniform<i8:f32:0,{2.000000e+02}>>

  %24 = "quantfork.stats"(%23) {layerStats = dense<[-1.0, 2.0]> : tensor<2xf32>} : (tensor<*xf32>) -> tensor<*xf32>
  func.return %24 : tensor<*xf32>

// CHECK-DAG: %[[input_0:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x1x5x!quant.uniform<i8:f32, 0.010588235481112611:-15>>) -> tensor<1x1x5xf32>
// CHECK-DAG: %[[input_1:.*]] = "tfl.dequantize"({{.*}}) : (tensor<2x5x!quant.uniform<i8<-127:127>:f32, 0.011122002376346137>>) -> tensor<2x5xf32>

    %cst_1 = "tfl.pseudo_qconst"() {qtype = tensor<1x20x!quant.uniform<i8:f32, 0.006:-34>>, value = dense<1> : tensor<1x20xi8>} : () -> tensor<1x20x!quant.uniform<i8:f32, 0.006:-34>>
    %cst_2 = "tfl.no_value"() {value = unit} : () -> none

func.func @all_keep_dim(%input: tensor<4x8xi1>) -> tensor<4x1xi1> {
  // CHECK: mhlo.reshape %{{.*}} : (tensor<4xi1>) -> tensor<4x1xi1>
  %dims = "tf.Const"() { value = dense<1> : tensor<1xi32>} : () -> tensor<1xi32>
  %0 = "tf.All"(%input, %dims) {keep_dims = true} : (tensor<4x8xi1>, tensor<1xi32>) -> tensor<4x1xi1>
  func.return %0 : tensor<4x1xi1>
}

// -----

// CHECK-LABEL: @all_dynamic

}

func.func @concat_v2_with_bool_type(%arg0: tensor<?x1xi1>, %arg1: tensor<?x1xi1>) -> tensor<?x2xi1> {
  %0 = "tf.Const"() { value = dense<-1> : tensor<i32> } : () -> tensor<i32>
  %1 = "tf.ConcatV2"(%arg0, %arg1, %0) : (tensor<?x1xi1>, tensor<?x1xi1>, tensor<i32>) -> tensor<?x2xi1>
  func.return %1 : tensor<?x2xi1>

// CHECK-LABEL: concat_v2_with_bool_type