}

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

func.func @testAllDimWrongRank(%input: tensor<4x6xi1>, %dims: tensor<2x2xi32>) {
  // expected-error @+1 {{dimensions can only be 0D or 1D tensor}}
  %0 = "tf.All"(%input, %dims) : (tensor<4x6xi1>, tensor<2x2xi32>) -> (tensor<*xi1>)
  func.return
}

// -----

func.func @testAllDimOutOfRange(%input: tensor<4x6xi1>) {

// -----

func.func @transpose(%arg0 : tensor<2x2xi32>, %arg1 : tensor<2xi32>) -> tensor<2x2xi32> {
  %0 = "tfl.transpose"(%arg0, %arg1) : (tensor<2x2xi32>, tensor<2xi32>) -> tensor<2x2xi32>
  func.return %0 : tensor<2x2xi32>
}

// -----

// CHECK-LABEL: transpose_with_output_that_has_dynamic_sizes
func.func @transpose_with_output_that_has_dynamic_sizes(%arg0 : tensor<2x2xi32>, %arg1 : tensor<2xi32>) -> tensor<?x?xi32> {

        %1 = "tf.OpB"() {_XlaSharding = "\08\03\1A\02\02\01\22\02\00\02"} : () -> tensor<2x2xi64>
        // expected-error @+1 {{should have the same sharding}}
        %2 = "tf.OpC"(%0, %1) {_xla_map_outside_compilation = "0", _xla_outside_compilation = "from_launch"} : (tensor<2x2xi64>, tensor<2x2xi64>) -> tensor<2x2xi64>
        "tf.OpD"(%2) : (tensor<2x2xi64>) -> ()
        tf_device.return

// CHECK: return %[[QUANT]]

// -----

// Tests that the pattern doesn't match when the input tensor's type is a
// quantized type.

func.func @uniform_quantize_op_quantized_input(%arg: tensor<2x2x!quant.uniform<i8:f32, 2.000000e+0:16>>) -> tensor<2x2x!quant.uniform<i8:f32, 3.000000e+0:127>> {

    %1 = "tf.Const"() {value = dense<2> : tensor<2x2xi32>} : () -> tensor<2x2xi32>
    %2 = "tf.Identity"(%1) {device = ""} : (tensor<2x2xi32>) -> tensor<2x2xi32>
    // CHECK: SpaceToBatchND{{.*}}-> tensor<4x98x130x128xf32>
    %3 = "tf.SpaceToBatchND"(%arg0, %0, %2) {device = ""} : (tensor<1x192x256x128xf32>, tensor<2xi32>, tensor<2x2xi32>) -> tensor<4x?x?x128xf32>
    func.return
  }

  %1 = "tf.Concat"(%0, %arg0, %arg1) {device = "/job:localhost/replica:0/task:0/device:GPU:0"} : (tensor<i32>, tensor<2x1xi32>, tensor<2x1xi32>) -> tensor<2x2xi32>
  func.return %1 : tensor<2x2xi32>
}

// CHECK-LABEL: testConcatCwiseUnary
func.func @testConcatCwiseUnary(%arg0: tensor<?x1xf32>, %arg1: tensor<?x1xf32>, %arg2: tensor<i32>) -> tensor<?x2xf32> {