class_docstr: A custom layer to scale the output of a QDense layer which used 'alpha != 1'
  Inference computation uses BatchNormalization methods
class_docstr_tok: ["A","custom","layer","to","scale","the","output","of","a","QDense","layer","which","used","'alpha","!","=","1'","Inference","computation","uses","BatchNormalization","methods"]
code: def initialize(self):
      inp = self.get_input_variable()
      shape = inp.shape
      dims = inp.dim_names
      self.add_output_variable(shape, dims)
  
      scale = self.get_attr("scale_data")
      scale_quantizer = self.get_attr("scale_quantizer")
      bias = self.get_attr("bias_data")
      bias_quantizer = self.get_attr("bias_quantizer")
  
      self.add_weights(scale, quantizer=scale_quantizer)
      self.add_bias(bias, quantizer=bias_quantizer)
  
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 ApplyAlpha obj's underlying member method code with example usage: obj.initialize() without return types