func.func @serving_default(%arg_0: tensor<5xf32> {tf_saved_model.index_path = ["input"]}) -> (tensor<5xf32> {tf_saved_model.index_path = ["output"]})
  attributes {tf.entry_function = {control_outputs = "", inputs = "inputs:0", outputs = "output:0"}, tf_saved_model.exported_names = ["serving_default"]} {
    return %arg_0 : tensor<5xf32>
  }
// CHECK: @serving_default(%[[ARG_0:.*]]: tensor<5xf32> {{.*}})

  func.return %4 : tensor<!tf_type.variant>
}
// CHECK-LABEL: __inference_Dataset_flat_map_lambda_190
func.func private @__inference_Dataset_flat_map_lambda_190(%arg0: tensor<i64> {tf._user_specified_name = "args_0"}) -> tensor<!tf_type.variant> attributes {tf._original_func_name = "__inference_Dataset_flat_map_lambda_19", tf._tf_data_function = true, tf.signature.is_stateful} {

    }
    tf_executor.fetch
  }
  func.return
}
// CHECK-LABEL: empty_island_multiple_data_results
// CHECK-SAME: (%[[ARG_0:.*]]: tensor<*xf32>, %[[ARG_1:.*]]: tensor<*xi32>)
// CHECK: %[[IDENTITY_N:.*]]:2 = "tf.IdentityN"
// CHECK-SAME: (%[[ARG_0]], %[[ARG_1]])
// CHECK: tf_executor.yield %[[IDENTITY_N]]#0, %[[IDENTITY_N]]#1

tensor<2x1xf32>, %arg6 as %arg15: tensor<*x!tf_type.resource<tensor<7x7x3x64xf32>>>, %arg8 as %arg16: tensor<*x!tf_type.resource<tensor<1001xf32>>>, %arg7 as %arg17: tensor<*x!tf_type.resource<tensor<64x1001xf32>>>, %arg9 as %arg18: tensor<*x!tf_type.resource<tensor<f32>>>, %arg10 as %arg19: tensor<*x!tf_type.resource<tensor<f32>>>, %arg11 as %arg20: tensor<*x!tf_type.resource<tensor<f32>>>, %arg12 as %arg21: tensor<*x!tf_type.resource<tensor<f32>>>) {_mirrored_variable_indices = [2, 3, 4, 5, 6, 7, 8],...

  %arg7: tensor<2048x640xf32>,
  %arg8: tensor<2048x640xf32>,
  %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>

    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "Tshape"
    value {
      type: DT_INT32
    }
  }
}

# MLIR-LABEL: func @main
# MLIR-SAME:  (%[[ARG_0:[a-z0-9]+]]: tensor<1x1x1x256x!quant.uniform<i8:f32, 0.21632751372549019:27>>) -> tensor<1x6x31x!quant.uniform<i8:f32, 0.09363494573854933:22>>
# MLIR-SAME:  control_outputs = ""
# MLIR-SAME:  inputs = "input"

        given:
        sourceFile << """
            #define ARGS_3 ("hello.h")
            #define FUNC_NAME() F
            #define F(X) X
            #define CONCAT_(X, Y) X ## Y
            #define CONCAT(X, Y) CONCAT_(X, Y)
            #define HEADER(X, Y, Z) CONCAT(X ## Y, ARGS_## Z)
            #include HEADER(FUNC_NAME, (), 3)
        """

        expect: