code: def register_bn_quant(backend):
      # Register the layer types to the layer map
      register_layer(
          "BatchNormalizationQuantizedTanh",
          BatchNormalizationQuantizedTanh,
      )
  
      # Register the optimization passes
      backend.register_pass(
          "merge_batch_norm_quantized_tanh",
          MergeBatchNormAndQuantizedTanh,
      )
      backend.register_pass(
          "quantize_dense_output", QuantizeDenseOutput
      )
  
      # Register template passes
      backend.register_template(
          BatchNormalizationQuantizedTanhConfigTemplate
      )
      backend.register_template(
          BatchNormalizationQuantizedTanhFunctionTemplate
      )
  
imports: import numpy
  from hls4ml.backends.fpga.fpga_layers import BatchNormalizationQuantizedTanh
  from hls4ml.backends.template import FunctionCallTemplate, LayerConfigTemplate
  from hls4ml.model.layers import BatchNormalization, register_layer
  from hls4ml.model.optimizer import OptimizerPass
  from hls4ml.model.types import IntegerPrecisionType, NamedType, XnorPrecisionType
label_desc: Use image node_id 1 for calling a global function with example usage: register_bn_quant(backend) without return types