code: def path_to_vector(path, categorical=False):
      """Converts path's commands to a series of vectors."""
      # Notes:
      #   - The SimpleSVG dataset does not have any 't', 'q', 'Z', 'T', or 'Q'.
      #     Thus, we don't handle those here.
      #   - We also removed all 'z's.
      #   - The x-axis-rotation argument to a commands is always 0 in this
      #     dataset, so we ignore it
  
      # Many commands have args that correspond to args in other commands.
      #   v  __,__ _______________ ______________,_________ __,__ __,__ _,y
      #   h  __,__ _______________ ______________,_________ __,__ __,__ x,_
      #   z  __,__ _______________ ______________,_________ __,__ __,__ _,_
      #   a  rx,ry x-axis-rotation large-arc-flag,sweepflag __,__ __,__ x,y
      #   l  __,__ _______________ ______________,_________ __,__ __,__ x,y
      #   c  __,__ _______________ ______________,_________ x1,y1 x2,y2 x,y
      #   m  __,__ _______________ ______________,_________ __,__ __,__ x,y
      #   s  __,__ _______________ ______________,_________ __,__ x2,y2 x,y
  
      # So each command will be converted to a vector where the dimension is the
      # minimal number of arguments to all commands:
      #   [rx, ry, large-arc-flag, sweepflag, x1, y1, x2, y2, x, y]
      # If a command does not output a certain arg, it is set to 0.
      #   "l 5,5" becomes [0, 0, 0, 0, 0, 0, 0, 0, 5, 5]
  
      # Also note, as of now we also output an extra dimension at index 0, which
      # indicates which command is being outputted (integer).
      new_path = []
      for cmd in path:
          new_path.append(cmd_to_vector(cmd, categorical=categorical))
      return new_path
  
imports: import io
  import numpy
  from PIL import Image
  from six.moves import zip_longest
  from skimage import draw
  import tensorflow.compat.v1
label_desc: Use image node_id 19 for calling a global function with example usage: path_to_vector(path, categorical) and returns: new_path