%arg9: tensor<2048xf32>,
  %arg10: tensor<2048xf32>,
  %arg11: tensor<2048xf32>,
  %arg12: tensor<2048xf32>,
  %arg13: tensor<640x2048xf32>,
  %arg14: tensor<640xf32>,
  %arg15: tensor<2048xf32>,
  %arg16: tensor<2048xf32>,
  %arg17: tensor<2048xf32>,
  %arg18: tensor<2048xf32>,
  %arg19: tensor<1x640xf32>,
  %arg20: tensor<1x2048xf32>
) -> tensor<1x640xf32> {

}

func.func @fully_connected(%arg0: tensor<1x37xf32>, %arg1: tensor<40x37xf32>, %arg2: tensor<40xf32>) -> tensor<1x40xf32> {
  // CHECK: _arithmetic_count = 3000 : i64
  %0 = "tfl.fully_connected"(%arg0, %arg1, %arg2) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<1x37xf32>, tensor<40x37xf32>, tensor<40xf32>) -> tensor<1x40xf32>
  func.return %0 : tensor<1x40xf32>
}

func.func @testPack(%arg0: tensor<4x8xf32>, %arg1: tensor<4x8xf32>) -> tensor<*xf32> {
  %0 = "tf.Pack"(%arg0, %arg1) {axis = 1 : i64} : (tensor<4x8xf32>, tensor<4x8xf32>) -> tensor<*xf32>
  func.return %0 : tensor<*xf32>
}


// -----

func.func @testPack(%arg0: tensor<4x8xf32>, %arg1: tensor<4x2xf32>) -> tensor<*xf32> {

  // CHECK: return %[[RESULT]]
  func.return %0: tensor<10x2xf32>
}

// -----

func.func @select_first(%arg0: tensor<10x2xf32>, %arg1: tensor<10x2xf32>) -> tensor<10x2xf32> {
  func.return %arg0: tensor<10x2xf32>
}

// CHECK-LABEL: testMultiInputLegacyCallOp
func.func @testMultiInputLegacyCallOp(%arg0: tensor<10x2xf32>, %arg1: tensor<10x2xf32>) -> tensor<10x2xf32> {

    %add_out, %add_control = tf_executor.island wraps "tf.AddV2"(%const_out, %const_out) {_xla_compile_device_type = "TPU", _replication_info = "cluster"} : (tensor<4x4xf32>, tensor<4x4xf32>) -> tensor<4x4xf32>
    %replicated_out, %replicated_control = tf_executor.island wraps "tf.TPUReplicatedOutput"(%add_out) : (tensor<4x4xf32>) -> (tensor<4x4xf32>)

func.func @no_change_to_dag(%arg0: tensor<!tf_type.resource<tensor<10x3xf32>>>, %arg1: tensor<!tf_type.resource<tensor<10x3xf32>>>, %arg2: tensor<!tf_type.resource<tensor<10x3xf32>>>, %arg3: tensor<!tf_type.resource<tensor<10x3xf32>>>) -> (tensor<!tf_type.resource<tensor<10x3xf32>>>, tensor<!tf_type.resource<tensor<10x3xf32>>>, tensor<!tf_type.resource...

    %0 = "tf.Identity"(%cst) {device = ""} : (tensor<i1>) -> tensor<i1>
    %1 = "tf.PartitionedCall"(%arg0, %cst_1, %cst_0) <{config = "", config_proto = "", executor_type = "", f = @composite_matmul_with_bias_fn_1}> {_tfl_quant_trait = "fully_quantizable"} : (tensor<1x4xf32>, tensor<4x3xf32>, tensor<3xf32>) -> tensor<1x3xf32>

  %2 = "tf.Const"() {value = dense<0.0> : tensor<2x2xf32>} : () -> tensor<2x2xf32>
  %3 = "tf.Const"() {value = dense<0.0> : tensor<4x4xf32>} : () -> tensor<4x4xf32>
  "tf.SendTPUEmbeddingGradients"(%2, %3) {_tpu_embedding_layer = "call1", config = "\0A\0B\0C\0D", operandSegmentSizes = array<i32: 2, 0>} : (tensor<2x2xf32>, tensor<4x4xf32>) -> ()

  func.func @set_dynamic_dimension_size_static_dim(%input: tensor<4x5xf32>, %size: tensor<i32>) -> tensor<*xf32> {
    %dimension = "tf.Const"() { value = dense<0> : tensor<i32> } : () -> tensor<i32>

    // CHECK: (tensor<4x5xf32>, tensor<i32>, tensor<i32>) -> tensor<?x5xf32>
    %0 = "tf.XlaSetDynamicDimensionSize"(%input, %dimension, %size) : (tensor<4x5xf32>, tensor<i32>, tensor<i32>) -> tensor<*xf32>
    func.return %0 : tensor<*xf32>

      tf_executor.yield %1, %1 : tensor<2048xf32>, tensor<2048xf32>
    }
    tf_executor.fetch %outputs#0, %outputs#1 : tensor<2048xf32>, tensor<2048xf32>
  }
  func.return %0#0, %0#1 : tensor<2048xf32>, tensor<2048xf32>
}
// CHECK-LABEL: func @single_op_island_duplicate_result
// CHECK: %[[CONST:.*]], %{{.*}} = tf_executor.island wraps "tf.Const"
// CHECK: tf_executor.fetch %[[CONST]], %[[CONST]]