class_docstr: Select N transforms to apply. Selected transforms will be called with `force_apply=True`.
  Transforms probabilities will be normalized to one 1, so in this case transforms probabilities works as weights.
  
  Args:
      transforms (list): list of transformations to compose.
      n (int): number of transforms to apply.
      replace (bool): Whether the sampled transforms are with or without replacement. Default: True.
      p (float): probability of applying selected transform. Default: 1.
class_docstr_tok: ["Select","N","transforms","to","apply",".","Selected","transforms","will","be","called","with","`","force_apply=True","`",".","Transforms","probabilities","will","be","normalized","to","one","1",",","so","in","this","case","transforms","probabilities","works","as","weights",".","Args",":","transforms","(","list",")",":","list","of","transformations","to","compose",".","n","(","int",")",":","number","of","transforms","to","apply",".","replace","(","bool",")",":","Whether","the","sampled","transforms","are","with","or","without","replacement",".","Default",":","True",".","p","(","float",")",":","probability","of","applying","selected","transform",".","Default",":","1","."]
code: def _to_dict(self) -> typing.Dict[str, typing.Any]:
      dictionary = super(SomeOf, self)._to_dict()
      dictionary.update({"n": self.n, "replace": self.replace})
      return dictionary
  
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 3 for calling the SomeOf obj's underlying member method code with example usage: obj._to_dict() and returns: dictionary