code: def cmd_to_vector(cmd_list, categorical=False):
      """Converts the given command (given as a list) into a vector."""
      # For description of how this conversion happens, see
      # path_to_vector docstring.
      cmd = cmd_list[0]
      args = cmd_list[1:]
  
      if not categorical:
          # integer, for MSE
          command = [float(CMD_MAPPING[cmd])]
      else:
          # one hot + 1 dim for EOS.
          command = [0.0] * (len(CMDS_LIST) + 1)
          command[CMD_MAPPING[cmd] + 1] = 1.0
  
      arguments = [0.0] * 10
      if cmd in "hH":
          arguments[8] = float(args[0])  # x
      elif cmd in "vV":
          arguments[9] = float(args[0])  # y
      elif cmd in "mMlLtT":
          arguments[8] = float(args[0])  # x
          arguments[9] = float(args[1])  # y
      elif cmd in "sSqQ":
          arguments[6] = float(args[0])  # x2
          arguments[7] = float(args[1])  # y2
          arguments[8] = float(args[2])  # x
          arguments[9] = float(args[3])  # y
      elif cmd in "cC":
          arguments[4] = float(args[0])  # x1
          arguments[5] = float(args[1])  # y1
          arguments[6] = float(args[2])  # x2
          arguments[7] = float(args[3])  # y2
          arguments[8] = float(args[4])  # x
          arguments[9] = float(args[5])  # y
      elif cmd in "aA":
          arguments[0] = float(args[0])  # rx
          arguments[1] = float(args[1])  # ry
          # we skip x-axis-rotation
          arguments[2] = float(args[3])  # large-arc-flag
          arguments[3] = float(args[4])  # sweep-flag
          # a does not have x1, y1, x2, y2 args
          arguments[8] = float(args[5])  # x
          arguments[9] = float(args[6])  # y
  
      return command + arguments
  
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 20 for calling a global function with example usage: cmd_to_vector(cmd_list, categorical) and returns: unknown