code: def parse_upsampling1d_layer(
      keras_layer, input_names, input_shapes, data_reader
  ):
      assert "UpSampling" in keras_layer["class_name"]
  
      layer = parse_default_keras_layer(keras_layer, input_names)
  
      layer["in_height"] = 1
      (layer["in_width"], layer["n_chan"]) = parse_data_format(
          input_shapes[0], layer["data_format"]
      )
  
      layer["algorithm"] = "nearest"
  
      layer["width_factor"] = keras_layer["config"]["size"]
  
      layer["out_height"] = 1
      layer["out_width"] = layer["in_width"] * layer["width_factor"]
  
      if layer["data_format"] == "channels_first":
          output_shape = [
              input_shapes[0][0],
              layer["n_chan"],
              layer["out_width"],
          ]
      else:
          output_shape = [
              input_shapes[0][0],
              layer["out_width"],
              layer["n_chan"],
          ]
  
      return layer, output_shape
  
file_path: hls4ml/hls4ml/converters/keras/reshape.py
imports: import numpy
  from hls4ml.converters.keras_to_hls import keras_handler, parse_default_keras_layer
  from hls4ml.converters.utils import parse_data_format
label_desc: Use image node_id 3 for calling a global function with example usage: parse_upsampling1d_layer(keras_layer, input_names, input_shapes, data_reader) and returns: layer,  output_shape