}
    }
  }
}
node {
  name: "func_call"
  op: "test_func_name"
  input: "x"
  attr {
    key: "_disable_call_shape_inference"
    value {
      b: true
    }
  }
  attr {
    key: "_tpu_replicate"
    value {
      s: "cluster"
    }
  }
}
library {
  function {
    signature {
      name: "test_func_name"
      input_arg {
        name: "a_0"
        type: DT_INT32

# `tf._resource_arg_unique_id` argument attribute of the function
# @test_func_name0.

# CHECK:  func @main
# CHECK:    tf_executor.graph
# CHECK:      "tf.VarHandleOp"()
# CHECK:      "tf.LegacyCall"
# CHECK-SAME:   <{_disable_call_shape_inference = true, f = @test_func_name0}> {device = ""}
# CHECK:      tf_executor.fetch
# CHECK:    return
# CHECK:  func private @test_func_name0
# CHECK-SAME:   tf._resource_arg_unique_id = 0

  tfg.func @test_func_name0(%test_func_name0: tensor<*x!tf_type.resource> {tf._output_shapes = #tf_type.shape<*>, tf._resource_arg_unique_id = 0 : i64, tfg.name = "test_func_name0"},
                            %test_func_name01: tensor<*x!tf_type.resource> {tf._output_shapes = #tf_type.shape<*>, tf._resource_arg_unique_id = 0 : i64, tfg.name = "test_func_name01"})

    tf_executor.fetch %outputs_0 : tensor<*x!tf_type.resource>
  }
  func.return %0 : tensor<*x!tf_type.resource>
}

                                   StringRef name_loc_id) {
    if (name_loc_id.contains("@")) {
      new_unit.set_node_name(name_loc_id.split('@').first.str());
      new_unit.set_func_name(name_loc_id.split('@').second.str());
    } else {
      new_unit.set_node_name(name_loc_id.str());
    }
  };

  ArrayRef<Location> locations = mlir::cast<FusedLoc>(loc).getLocations();

    // Fetch func_name and node_name from attributes and save as proto.
    quantization::UnitWiseQuantizationSpec::QuantizationUnit quant_unit_proto;
    quant_unit_proto.set_func_name(func_name);
    quant_unit_proto.set_node_name(node_name);

    string quant_unit_path = io::JoinPath(log_dir_path, "quant_unit.pb");
    OP_REQUIRES_OK(