docstr: Applies one of the transforms to the image
  
  @param image: PIL Image to be augmented
  
  @param metadata: if set to be a list, metadata about the function execution
      including its name, the source & dest width, height, etc. will be appended to
      the inputted list. If set to None, no metadata will be appended or returned
  
  @param bboxes: a list of bounding boxes can be passed in here if desired. If
      provided, this list will be modified in place such that each bounding box is
      transformed according to this function
  
  @param bbox_format: signifies the type of bounding box that was passed in in `bboxes`.
      Must specify `bbox_type` if `bboxes` is provided. Supported bbox_format values
      are "pascal_voc", "pascal_voc_norm", "coco", and "yolo"
  
  @returns: Augmented PIL Image
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code: def __call__(
      self,
      image: Image.Image,
      metadata: Optional[List[Dict[str, Any]]] = None,
      bboxes: Optional[List[Tuple]] = None,
      bbox_format: Optional[str] = None,
  ) -> Image.Image:
      """
      Applies one of the transforms to the image
  
      @param image: PIL Image to be augmented
  
      @param metadata: if set to be a list, metadata about the function execution
          including its name, the source & dest width, height, etc. will be appended to
          the inputted list. If set to None, no metadata will be appended or returned
  
      @param bboxes: a list of bounding boxes can be passed in here if desired. If
          provided, this list will be modified in place such that each bounding box is
          transformed according to this function
  
      @param bbox_format: signifies the type of bounding box that was passed in in `bboxes`.
          Must specify `bbox_type` if `bboxes` is provided. Supported bbox_format values
          are "pascal_voc", "pascal_voc_norm", "coco", and "yolo"
  
      @returns: Augmented PIL Image
      """
      if random.random() > self.p:
          return image
  
      transform = random.choices(self.transforms, self.transform_probs)[
          0
      ]
      return transform(
          image,
          force=True,
          metadata=metadata,
          bboxes=bboxes,
          bbox_format=bbox_format,
      )
  
file_path: AugLy/augly/image/composition.py
imports: import random
  from typing import Any, Dict, List, Optional, Tuple
  from augly.image.transforms import BaseTransform
  from PIL import Image
label_desc: Use image node_id 2 for calling the OneOf obj's underlying member method code with example usage: obj.__call__(image, metadata, bboxes, bbox_format) and returns: transform, image