class_docstr: Apply AutoAugment :cite:`cubuk2018autoaugment` searched strategies.
  
  Args:
      policy: a customized policy config or presets of "imagenet", "cifar10", and "svhn".
      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 torch
      >>> import kornia.augmentation as K
      >>> in_tensor = torch.rand(5, 3, 30, 30)
      >>> aug = K.AugmentationSequential(AutoAugment())
      >>> aug(in_tensor).shape
      torch.Size([5, 3, 30, 30])
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code: def compose_subpolicy_sequential(
      self, subpolicy: SUBPLOLICY_CONFIG
  ) -> PolicySequential:
      return PolicySequential(
          *[
              getattr(ops, name)(prob, mag)
              for name, prob, mag in subpolicy
          ]
      )
  
file_path: kornia/kornia/augmentation/auto/autoaugment/autoaugment.py
imports: from typing import Iterator, List, Optional, Tuple, Union
  from torch.distributions import Categorical
  from kornia.augmentation.auto.base import SUBPLOLICY_CONFIG, PolicyAugmentBase
  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 2 for calling the AutoAugment obj's underlying member method code with example usage: obj.compose_subpolicy_sequential(subpolicy) and returns: PolicySequential