code: def _setup_transforms(self, n_aggregators, n_filters, n_propagate):
      if self._quantize_transforms:
          self._input_feature_transform = NamedQDense(
              n_propagate,
              kernel_quantizer="quantized_bits(%i,%i,0,alpha=1)"
              % (self._total_bits, self._int_bits),
              bias_quantizer="quantized_bits(%i,%i,0,alpha=1)"
              % (self._total_bits, self._int_bits),
              name="FLR",
          )
          self._output_feature_transform = NamedQDense(
              n_filters,
              kernel_quantizer="quantized_bits(%i,%i,0,alpha=1)"
              % (self._total_bits, self._int_bits),
              name="Fout",
          )
          if (
              self._output_activation is None
              or self._output_activation == "linear"
          ):
              self._output_activation_transform = QActivation(
                  "quantized_bits(%i, %i)"
                  % (self._total_bits, self._int_bits)
              )
          else:
              self._output_activation_transform = QActivation(
                  "quantized_%s(%i, %i)"
                  % (
                      self._output_activation,
                      self._total_bits,
                      self._int_bits,
                  )
              )
      else:
          self._input_feature_transform = NamedDense(
              n_propagate, name="FLR"
          )
          self._output_feature_transform = NamedDense(
              n_filters, activation=self._output_activation, name="Fout"
          )
          self._output_activation_transform = keras.layers.Activation(
              self._output_activation
          )
  
      self._aggregator_distance = NamedDense(n_aggregators, name="S")
  
      self._sublayers = [
          self._input_feature_transform,
          self._aggregator_distance,
          self._output_feature_transform,
          self._output_activation_transform,
      ]
  
imports: import tensorflow.keras
  from qkeras import QActivation, QDense, ternary
label_desc: Use image node_id 3 for calling the GarNet obj's underlying member method code with example usage: obj._setup_transforms(n_aggregators, n_filters, n_propagate) without return types