code: def parse_embedding_layer(
      keras_layer, input_names, input_shapes, data_reader
  ):
      assert "Embedding" in keras_layer["class_name"]
  
      layer = parse_default_keras_layer(keras_layer, input_names)
  
      layer["n_in"] = input_shapes[0][1]
      layer["vocab_size"] = keras_layer["config"]["input_dim"]
      layer["n_out"] = keras_layer["config"]["output_dim"]
  
      layer["embeddings_data"] = get_weights_data(
          data_reader, layer["name"], "embeddings"
      )
  
      output_shape = input_shapes[0] + [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 5 for calling a global function with example usage: parse_embedding_layer(keras_layer, input_names, input_shapes, data_reader) and returns: layer,  output_shape