class_docstr: Do transformation on input data with corresponding pre-processing and augmentation operations.
  The shape of input data to all operations is [height, width, channels].
class_docstr_tok: ["Do","transformation","on","input","data","with","corresponding","pre-processing","and","augmentation","operations",".","The","shape","of","input","data","to","all","operations","is","[","height",",","width",",","channels","]","."]
code: def __init__(self, transforms: Callable, to_rgb: bool = True):
      if not isinstance(transforms, list):
          raise TypeError("The transforms must be a list!")
      self.transforms = transforms
      self.to_rgb = to_rgb
  
imports: import random
  import base64
  from typing import Callable, Union, List, Tuple
  import cv2
  import numpy
  import paddle
  import paddle.nn.functional
  from paddleseg.transforms import functional
  from PIL import Image
label_desc: Use image node_id 1 to create a new Compose object  with example: obj = Compose(transforms, to_rgb)