class_docstr: Apply RandAugment :cite:`cubuk2020randaugment` augmentation strategies.
  
  Args:
      n: the number of augmentations to apply sequentially.
      m: magnitude for all the augmentations, ranged from [0, 30].
      policy: candidate transformations. If None, a default candidate list will be used.
      transformation_matrix_mode: computation mode for the chained transformation matrix, via `.transform_matrix`
                                  attribute.
                                  If `silent`, transformation matrix will be computed silently and the non-rigid
                                  modules will be ignored as identity transformations.
                                  If `rigid`, transformation matrix will be computed silently and the non-rigid
                                  modules will trigger errors.
                                  If `skip`, transformation matrix will be totally ignored.
  
  Examples:
      >>> import kornia.augmentation as K
      >>> in_tensor = torch.rand(5, 3, 30, 30)
      >>> aug = K.AugmentationSequential(RandAugment(n=2, m=10))
      >>> aug(in_tensor).shape
      torch.Size([5, 3, 30, 30])
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code: def get_forward_sequence(
      self, params: Optional[List[ParamItem]] = None
  ) -> Iterator[Tuple[str, Module]]:
      if params is None:
          idx = self.rand_selector.sample((self.n,))
          return self.get_children_by_indices(idx)
  
      return self.get_children_by_params(params)
  
file_path: kornia/kornia/augmentation/auto/rand_augment/rand_augment.py
imports: from typing import Dict, Iterator, List, Optional, Tuple, Union, cast
  import torch
  from torch.distributions import Categorical
  from kornia.augmentation.auto.base import SUBPLOLICY_CONFIG, PolicyAugmentBase
  from kornia.augmentation.auto.operations import OperationBase
  from kornia.augmentation.auto.operations.policy import PolicySequential
  from kornia.augmentation.container.params import ParamItem
  from kornia.core import Module, Tensor
  from .None import ops
label_desc: Use image node_id 3 for calling the RandAugment obj's underlying member method code with example usage: obj.get_forward_sequence(params) and returns: self, self