code: def get_transforms_config(self, node, params):
      params["n_in_features"] = node.attributes["n_in_features"]
      params["n_propagate"] = node.attributes["n_propagate"]
      params["n_aggregators"] = node.get_weights(
          "aggregator_distance_biases"
      ).shape[0]
      params["n_out_features"] = node.get_weights(
          "output_transform_biases"
      ).shape[0]
  
      for wname, weights in node.weights.items():
          params[wname] = weights.name
          params[f"{wname}_t"] = weights.type.name
          params[f"{wname}_size"] = weights.data_length
  
imports: import numpy
  from hls4ml.backends.fpga.fpga_types import APTypeConverter
  from hls4ml.backends.template import FunctionCallTemplate, LayerConfigTemplate
  from hls4ml.model.layers import GarNet, GarNetStack
  from hls4ml.model.types import FixedPrecisionType
label_desc: Use image node_id 2 for calling the GarNetConfigTemplate obj's underlying member method code with example usage: obj.get_transforms_config(node, params) without return types