code: def parse_input_layer(
      keras_layer, input_names, input_shapes, data_reader
  ):
      assert keras_layer["class_name"] == "InputLayer"
  
      layer = parse_default_keras_layer(keras_layer, input_names)
  
      layer["input_shape"] = keras_layer["config"]["batch_input_shape"][
          1:
      ]
  
      dtype = keras_layer["config"]["dtype"]
      if dtype.startswith("int") or dtype.startswith("uint"):
          layer["type_name"] = "integer_input_t"
          width = int(dtype[dtype.index("int") + 3 :])
          signed = not dtype.startswith("u")
          layer["precision"] = IntegerPrecisionType(
              width=width, signed=signed
          )
      # elif bool, q[u]int, ...
  
      output_shape = keras_layer["config"]["batch_input_shape"]
  
      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 1 for calling a global function with example usage: parse_input_layer(keras_layer, input_names, input_shapes, data_reader) and returns: layer,  output_shape