code: def parse_dense_layer(
      keras_layer, input_names, input_shapes, data_reader
  ):
      assert "Dense" in keras_layer["class_name"]
  
      layer = parse_default_keras_layer(keras_layer, input_names)
  
      layer["weight_data"], layer["bias_data"] = get_weights_data(
          data_reader, layer["name"], ["kernel", "bias"]
      )
      layer["n_in"] = layer["weight_data"].shape[0]
      layer["n_out"] = layer["weight_data"].shape[1]
      if "Binary" in layer["class_name"]:
          layer["weight_quantizer"] = BinaryQuantizer(bits=2)
          layer["bias_quantizer"] = BinaryQuantizer(bits=2)
      elif "Ternary" in layer["class_name"]:
          layer["weight_quantizer"] = TernaryQuantizer()
          layer["bias_quantizer"] = TernaryQuantizer()
      else:
          layer["weight_quantizer"] = None
          layer["bias_quantizer"] = None
      output_shape = input_shapes[0][:]
      output_shape[-1] = layer["n_out"]
  
      return layer, output_shape
  
imports: from hls4ml.converters.keras_to_hls import get_weights_data, keras_handler, parse_default_keras_layer
  from hls4ml.model.types import BinaryQuantizer, IntegerPrecisionType, TernaryQuantizer
label_desc: Use image node_id 2 for calling a global function with example usage: parse_dense_layer(keras_layer, input_names, input_shapes, data_reader) and returns: layer,  output_shape