class_docstr: A Transform is defined as a list of transform definitions, which are
  applied in the order provided. The individual transform definitions are
  separated by whitespace and/or a comma.
class_docstr_tok: ["A","Transform","is","defined","as","a","list","of","transform","definitions",",","which","are","applied","in","the","order","provided",".","The","individual","transform","definitions","are","separated","by","whitespace","and\/or","a","comma","."]
code: def __init__(self, content=""):
      self._transforms = []
      if not content:
          return
  
      converters = {
          "matrix": Matrix,
          "scale": Scale,
          "rotate": Rotate,
          "translate": Translate,
          "skewx": SkewX,
          "skewy": SkewY,
      }
      keys = "|".join(converters.keys())
      pattern = r"(?P<name>%s)\s*\((?P<args>[^)]*)\)" % keys
  
      for match in re.finditer(pattern, content):
          name = match.group("name").strip()
          args = match.group("args").strip().replace(",", " ")
          args = [float(value) for value in args.split()]
          transform = converters[name](*args)
          self._transforms.append(transform)
  
imports: import re
  import math
  import numpy
label_desc: Use image node_id 1 to create a new Transform object from inherited base classes: object with example: obj = Transform(content)