code: def transform(self, model, node):
      bn_layer = node.get_input_node()
      # Make a new layer with the new attributes
      quantize = 0
      if "binary" in node.get_attr("activation"):
          quantize = 2
      if "ternary" in node.get_attr("activation"):
          quantize = 3
      attrs = {
          "name": bn_layer.get_attr("name"),
          "original_name": bn_layer.get_attr("name"),
          "class_name": "BatchNormalizationQuantizedTanh",
          "n_in": bn_layer.get_attr("n_in"),
          "n_out": bn_layer.get_attr("n_in"),
          "n_filt": bn_layer.get_attr("n_filt"),
          "quantize": quantize,
          "trace": bn_layer.get_attr("trace"),
      }
      bnbt_layer = model.make_node(
          BatchNormalizationQuantizedTanh,
          "bnbt_" + bn_layer.name,
          attrs,
          bn_layer.inputs,
      )
      bnbt_layer.set_thresholds(
          bn_layer.get_weights("scale").data,
          bn_layer.get_weights("bias").data,
          node.get_attr("threshold", 0.5),
      )
      # Remove the BatchNormalization layer
      model.remove_node(bn_layer, rewire=True)
      # Replace the old Activation layer with this one
      model.replace_node(node, bnbt_layer)
  
      return True
  
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 2 for calling the MergeBatchNormAndQuantizedTanh obj's underlying member method code with example usage: obj.transform(model, node) and returns: True