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].
  
  Args:
      transforms (list): A list contains data pre-processing or augmentation.
      to_rgb (bool, optional): If converting image to RGB color space. Default: True.
  
  Raises:
      TypeError: When 'transforms' is not a list.
      ValueError: when the length of 'transforms' is less than 1.
code: def __init__(self, transforms: Callable, to_rgb: bool = True):
      if not isinstance(transforms, list):
          raise TypeError("The transforms must be a list!")
      if len(transforms) < 1:
          raise ValueError(
              "The length of transforms "
              + "must be equal or larger than 1!"
          )
      self.transforms = transforms
      self.to_rgb = to_rgb
  
imports: import os
  import random
  from typing import Callable, Union, List, Tuple
  import cv2
  import numpy
  from PIL import Image
  import paddlehub.vision.functional
label_desc: Use image node_id 1 to create a new Compose object  with example: obj = Compose(transforms, to_rgb)