code: def parse_zeropadding1d_layer(
      keras_layer, input_names, input_shapes, data_reader
  ):
      assert keras_layer["class_name"] == "ZeroPadding1D"
  
      layer = parse_default_keras_layer(keras_layer, input_names)
  
      padding = keras_layer["config"]["padding"]
      if isinstance(padding, int):
          layer["pad_left"] = padding
          layer["pad_right"] = padding
      elif isinstance(padding, collections.abc.Sequence):
          layer["pad_left"] = padding[0]
          layer["pad_right"] = padding[1]
  
      if layer["data_format"] == "channels_first":
          output_shape = [
              input_shapes[0][0],  # Batch
              input_shapes[0][1],  # Channels
              layer["pad_left"]
              + input_shapes[0][2]
              + layer["pad_right"],  # Width
          ]
          layer["out_width"] = output_shape[2]
          layer["n_chan"] = output_shape[1]
  
          layer["in_width"] = input_shapes[0][2]
      else:
          output_shape = [
              input_shapes[0][0],  # Batch
              layer["pad_left"]
              + input_shapes[0][1]
              + layer["pad_right"],  # Width
              input_shapes[0][2],  # Channels
          ]
          layer["out_width"] = output_shape[1]
          layer["n_chan"] = output_shape[2]
  
          layer["in_width"] = input_shapes[0][1]
  
      return layer, output_shape
  
label_desc: Use image node_id 1 for calling a global function with example usage: parse_zeropadding1d_layer(keras_layer, input_names, input_shapes, data_reader) and returns: layer,  output_shape