class_docstr: OptimizerPass to merge consecutive BatchNormalization layers.
  These may exist in a model after QKerasFactorizeAlpha layer.
  Scale and Bias of each layer are combined into scale and bias of a single layer.
class_docstr_tok: ["OptimizerPass","to","merge","consecutive","BatchNormalization","layers",".","These","may","exist","in","a","model","after","QKerasFactorizeAlpha","layer",".","Scale","and","Bias","of","each","layer","are","combined","into","scale","and","bias","of","a","single","layer","."]
code: def match(self, node):
      return isinstance(node, BatchNormalization) and isinstance(
          node.get_input_node(), BatchNormalization
      )
  
imports: import numpy
  import tensorflow
  from hls4ml.model.layers import BatchNormalization, register_layer
  from hls4ml.model.optimizer import ConfigurableOptimizerPass, OptimizerPass, register_pass
  from hls4ml.model.types import FixedPrecisionType, IntegerPrecisionType, NamedType, QKerasPO2Quantizer
label_desc: Use image node_id 1 for calling the FuseConsecutiveBatchNormalization obj's underlying member method code with example usage: obj.match(node) and returns: unknown