func.func @testBroadcastGradientArgsInvalidS0Rank() -> (tensor<2x2xi32>, tensor<0xi32>) {
  %s0 = "tf.Const"() {value = dense<[[4, 1], [2, 3]]> : tensor<2x2xi32>} : () -> tensor<2x2xi32>
  %s1 = "tf.Const"() {value = dense<[2, 4]> : tensor<2xi32>} : () -> tensor<2xi32>
  // expected-error @+1 {{failed to verify that operand 0 is 1-D}}
  %r0, %r1 = "tf.BroadcastGradientArgs"(%s0, %s1) : (tensor<2x2xi32>, tensor<2xi32>) -> (tensor<1xi32>, tensor<0xi32>)

// -----

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> {

func.func @pack2Tensors(%arg0: tensor<2xi32>, %arg1: tensor<2xi32>) -> tensor<2x2xi32> {
  %0 = "tf.Pack"(%arg0, %arg1) : (tensor<2xi32>, tensor<2xi32>) -> tensor<2x2xi32>
  func.return %0 : tensor<2x2xi32>

// CHECK-LABEL: pack2Tensors
// CHECK: "tfl.pack"(%arg0, %arg1) <{axis = 0 : i32, values_count = 2 : i32}> : (tensor<2xi32>, tensor<2xi32>) -> tensor<2x2xi32>
}

// 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.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> {