class_docstr: Compose several transforms together.
  
  Args:
      transforms: list of transforms to compose.
  
  Example:
      >>> transforms.Compose([
      >>>     transforms.ToTensor(),
      >>>     transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
      >>> ])
docstr: Args:
      img (PIL Image or numpy array): Image to be processed.
  
  Returns:
      PIL Image or numpy array: Processed image.
docstr_tok: ["Args",":","img","(","PIL","Image","or","numpy","array",")",":","Image","to","be","processed",".","Returns",":","PIL","Image","or","numpy","array",":","Processed","image","."]
code: def forward(self, img):
      """
      Args:
          img (PIL Image or numpy array): Image to be processed.
  
      Returns:
          PIL Image or numpy array: Processed image.
      """
      for t in self.transforms:
          img = t.forward(img)
      return img
  
file_path: singa/examples/demos/Classification/BloodMnist/transforms.py
imports: import numpy
  from PIL import Image
label_desc: Use image node_id 2 for calling the Compose obj's underlying member method code with example usage: obj.forward(img) and returns: img