class_docstr: Sequentially applies all transforms to targets.
  
  Note:
      This transform is not intended to be a replacement for `Compose`. Instead, it should be used inside `Compose`
      the same way `OneOf` or `OneOrOther` are used. For instance, you can combine `OneOf` with `Sequential` to
      create an augmentation pipeline that contains multiple sequences of augmentations and applies one randomly
      chose sequence to input data (see the `Example` section for an example definition of such pipeline).
  
  Example:
      >>> import albumentations as A
      >>> transform = A.Compose([
      >>>    A.OneOf([
      >>>        A.Sequential([
      >>>            A.HorizontalFlip(p=0.5),
      >>>            A.ShiftScaleRotate(p=0.5),
      >>>        ]),
      >>>        A.Sequential([
      >>>            A.VerticalFlip(p=0.5),
      >>>            A.RandomBrightnessContrast(p=0.5),
      >>>        ]),
      >>>    ], p=1)
      >>> ])
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code: def __call__(self, *args, **data) -> typing.Dict[str, typing.Any]:
      for t in self.transforms:
          data = t(**data)
      return data
  
imports: from __future__ import division
  import random
  import typing
  import warnings
  from collections import defaultdict
  import numpy
  from ..None import random_utils
  from .bbox_utils import BboxParams, BboxProcessor
  from .keypoints_utils import KeypointParams, KeypointsProcessor
  from .serialization import SERIALIZABLE_REGISTRY, Serializable, get_shortest_class_fullname, instantiate_nonserializable
  from .transforms_interface import BasicTransform
  from .utils import format_args, get_shape
label_desc: Use image node_id 2 for calling the Sequential obj's underlying member method code with example usage: obj.__call__() and returns: data