%0 = "tfl.pseudo_const"() {value = dense<0> : tensor<3xi32>} : () -> tensor<3xi32>
  %1 = "tfl.pseudo_const"() {value = dense<[1, 384, 32]> : tensor<3xi32>} : () -> tensor<3xi32>
  %2 = func.call @simple_test(%arg0, %0, %1) {tac.interface_name = "func1"} : (tensor<4x384x32xf32>, tensor<3xi32>, tensor<3xi32>) -> tensor<1x384x32xf32>
  func.return %2 : tensor<1x384x32xf32>
}

// PARTIAL-LABEL: @simple_test

  %4 = "tfl.minimum"(%0, %cst) : (tensor<32xf32>, tensor<32xf32>) -> tensor<32xf32>
  %5 = "tfl.minimum"(%1, %cst) : (tensor<32xf32>, tensor<32xf32>) -> tensor<32xf32>
  %6 = "tfl.minimum"(%2, %cst) : (tensor<32xf32>, tensor<32xf32>) -> tensor<32xf32>
  %7 = "tfl.minimum"(%3, %cst) : (tensor<32xf32>, tensor<32xf32>) -> tensor<32xf32>

  func.return %0#0 : tensor<2xi32>

// CHECK-LABEL: unpack3Tensors
// CHECK: "tfl.unpack"(%arg0) <{axis = 1 : i32, num = 3 : i32}> : (tensor<2x3xi32>) -> (tensor<2xi32>, tensor<2xi32>, tensor<2xi32>)
}

func.func @unpackNegAxis(%arg0: tensor<2x3xi32>) -> tensor<2xi32> {
  %0:3 = "tf.Unpack"(%arg0) {axis = -1 : i64} : (tensor<2x3xi32>) -> (tensor<2xi32>, tensor<2xi32>, tensor<2xi32>)

      Targs = [tensor<32xf32>, tensor<64xf32>, tensor<16xf32>],
      Tresults = [tensor<32xf32>, tensor<16xf32>]}
      : (tensor<32xf32>, tensor<64xf32>, tensor<16xf32>, tensor<2x!tf_type.string>) -> (tensor<32xf32>, tensor<16xf32>)
    tf_device.return %0#0, %0#1 : tensor<32xf32>, tensor<16xf32>
  }) {device = "/job:localhost/replica:0/task:0/device:TPU:0"} : () -> (tensor<32xf32>, tensor<16xf32>)
  // CHECK-NEXT: tf_device.return

// -----

func.func @testFusedActivationFunction(%arg0: tensor<4xi32>, %arg1: tensor<4xi32>) -> (tensor<4xi32>, tensor<4xi32>, tensor<4xi32>, tensor<4xi32>, tensor<4xi32>, tensor<4xi32>) {
  // CHECK: "NONE"
  %0 = tfl.add %arg0, %arg1 {fused_activation_function = "NONE"} : tensor<4xi32>
  // CHECK: "RELU"
  %1 = tfl.add %arg0, %arg1 {fused_activation_function = "RELU"} : tensor<4xi32>
  // CHECK: "RELU_N1_TO_1"

  // CHECK-DAG: [[OFFSET_2:%.*]] = "tf.Const{{.*}} dense<[0, 5, 0]> : tensor<3xi32>

  %offset:3 = "tf.ConcatOffset"(%concat_dim, %shape0, %shape1, %shape2) : (tensor<i32>, tensor<3xi32>, tensor<3xi32>, tensor<3xi32>) -> (tensor<3xi32>, tensor<3xi32>, tensor<3xi32>)

  // CHECK: return [[OFFSET_0]], [[OFFSET_1]], [[OFFSET_2]]
  func.return %offset#0, %offset#1, %offset#2: tensor<3xi32>, tensor<3xi32>, tensor<3xi32>
}

// CHECK-LABEL: func @identity
func.func @identity(%arg0: tensor<1xi32>) -> tensor<1xi32> {
  // CHECK-NEXT:  return %arg0 : tensor<1xi32>
  %0 = "tf.Identity"(%arg0) : (tensor<1xi32>) -> tensor<1xi32>
  func.return %0: tensor<1xi32>
}

// -----

// CHECK-LABEL: func @identityN
func.func @identityN(%arg0: tensor<1xi32>, %arg1: tensor<1xf32>) -> (tensor<1xi32>, tensor<1xf32>) {

func.func @testReshapeShapeInference(%arg0: tensor<3x4xi32>) -> tensor<*xi32> {
  %cst = arith.constant dense<[1, 6, 2]> : tensor<3xi32>
  // CHECK: "tfl.reshape"(%arg0, %cst) : (tensor<3x4xi32>, tensor<3xi32>) -> tensor<1x6x2xi32>
  %0 = "tfl.reshape"(%arg0, %cst) : (tensor<3x4xi32>, tensor<3xi32>) -> tensor<*xi32>
  func.return %0 : tensor<*xi32>
}
}

// -----

  func.return %2 : tensor<1x128x32xf32>

// CHECK-DAG:  [[VAL_1:%.*]] = arith.constant dense<0> : tensor<3xi32>
// CHECK-DAG:  [[VAL_2:%.*]] = arith.constant dense<[1, 128, 32]> : tensor<3xi32>
// CHECK:  [[VAL_3:%.*]] = "tfl.slice"(%arg0, [[VAL_1]], [[VAL_2]]) : (tensor<4x128x32xf32>, tensor<3xi32>, tensor<3xi32>) -> tensor<1x128x32xf32>
}

// -----

// CHECK-LABEL: @WhileCanonicalizeBug

  %cst_perm = arith.constant dense<0> : tensor<1xi32>

  // CHECK: %[[CST:.*]] = arith.constant dense<{{\[}}1, 2, 3]> : tensor<3xi32>
  // CHECK: return %[[CST]]
  %0 = "tfl.transpose"(%cst, %cst_perm) : (tensor<3xi32>, tensor<1xi32>) -> tensor<3xi32>
  func.return %0 : tensor<3xi32>
}

// CHECK-LABEL: @transpose_dynamic
func.func @transpose_dynamic() -> tensor<?xi32> {
  %cst = arith.constant dense<[1, 2, 3]> : tensor<3xi32>