%0:3 = "tf.Unpack"(%input) {axis = 1} : (tensor<4x3x6xf32>) -> (tensor<4x6xf32>, tensor<4x6xf32>, tensor<4x6xf32>)
  // return %[[RES1]], %[[RES2]], %[[RES3]]
  func.return %0#0, %0#1, %0#2 : tensor<4x6xf32>, tensor<4x6xf32>, tensor<4x6xf32>
}

// -----

// CHECK-LABEL: func @unpack_dynamic
func.func @unpack_dynamic(%arg0: tensor<?x?x2xf32>) -> (tensor<?x?xf32>, tensor<?x?xf32>) {

  func.return %0 : tensor<8x16xf32>

// CHECK-LABEL:minimum
// CHECK:  "tfl.minimum"(%arg0, %arg1) : (tensor<8x16xf32>, tensor<8x16xf32>) -> tensor<8x16xf32>
}

func.func @realDiv(%arg0: tensor<8x16xf32>, %arg1: tensor<8x16xf32>) -> tensor<8x16xf32> {
  %0 = "tf.RealDiv"(%arg0, %arg1) : (tensor<8x16xf32>, tensor<8x16xf32>) -> tensor<8x16xf32>

}

  // CHECK: return %0
}

// CHECK-LABEL: testAddOfNegRight
func.func @testAddOfNegRight(%arg0: tensor<8x16xf32>, %arg1: tensor<8x16xf32>) -> tensor<8x16xf32> {
  %0 = "tf.Neg"(%arg1) : (tensor<8x16xf32>) -> tensor<8x16xf32>
  %1 = "tf.Add"(%arg0, %0) {device = "/job:localhost/replica:0/task:0/device:GPU:0"} : (tensor<8x16xf32>, tensor<8x16xf32>) -> tensor<8x16xf32>

  // CHECK: %[[MATMUL_PACKED:.*]] = "tf.Pack"(%[[MATMUL_1]], %[[MATMUL_2]], %[[MATMUL_3]], %[[MATMUL_4]], %[[MATMUL_5]], %[[MATMUL_6]]) <{axis = 0 : i64}> : (tensor<4x6xf32>, tensor<4x6xf32>, tensor<4x6xf32>, tensor<4x6xf32>, tensor<4x6xf32>, tensor<4x6xf32>) -> tensor<6x4x6xf32>
  // CHECK: %[[RESULT:.*]] = "tf.Reshape"(%[[MATMUL_PACKED]], %[[RESULT_SHAPE]]) : (tensor<6x4x6xf32>, tensor<4xi64>) -> tensor<2x3x4x6xf32>

    %input:2 = tf_executor.island wraps "tf.Const"() {dtype = "tftype$DT_INT32", value = dense<1.0> : tensor<4x6xf32>} : () -> tensor<4x6xf32>
    %split:3 = tf_executor.island wraps "tf.Split"(%dim#0, %input#0) : (tensor<i32>, tensor<4x6xf32>) -> (tensor<2x6xf32>, tensor<2x6xf32>)
    tf_executor.fetch
  }
  func.return

  %0:4 = "tfl.basic_lstm"(%arg0, %arg1, %arg2, %arg3, %arg4) {fused_activation_function = "RELU", cell_clip = 1.0 : f32, proj_clip = 2.0 : f32} : (tensor<1x384xf32>, tensor<1x96xf32>, tensor<384x480xf32>, tensor<384xf32>, tensor<1x96xf32>) -> (tensor<1x96xf32>, tensor<1x96xf32>, tensor<1x480xf32>, tensor<1x384xf32>)
  func.return %0#0 : tensor<1x96xf32>

}

// CHECK-LABEL: softmax
func.func @softmax(%arg0: tensor<8x16xf32>) -> tensor<8x16xf32> {
  %0 = "tf.Softmax"(%arg0) : (tensor<8x16xf32>) -> tensor<8x16xf32>
  func.return %0 : tensor<8x16xf32>
// CHECK: %[[SOFTMAX_0:.*]] = "tf.Softmax"(%arg0) : (tensor<8x16xf32>) -> tensor<8x16xf32>
// CHECK: return %[[SOFTMAX_0]] : tensor<8x16xf32>
}

// CHECK-LABEL: conv2d_backprop_input_with_add

// CHECK-NEXT:  [[NEG_ONE_1:%.*]] = arith.constant dense<-1> : tensor<i32>
// CHECK-NEXT:  [[RESULT:%.*]] = "tf.Concat"([[ZERO]], [[INPUT]], [[EXTENDED_PART]]) : (tensor<i32>, tensor<3x10xf32>, tensor<?x10xf32>) -> tensor<?x10xf32>
// CHECK-NEXT:  return [[RESULT]] : tensor<?x10xf32>


// CHECK-LABEL:  func private @cond_false

// CHECK:           %[[VAL_5:.*]] = "tf.Select"(%[[VAL_3]], %[[VAL_0]], %[[VAL_4]]) : (tensor<?x?xi1>, tensor<4x8xf32>, tensor<4x8xf32>) -> tensor<4x8xf32>
// CHECK:           return %[[VAL_5]] : tensor<4x8xf32>
// CHECK:         }
func.func @relu_grad(%arg0: tensor<4x8xf32>, %arg1: tensor<?x?xf32>) -> tensor<4x8xf32> {