class_docstr: Apply TrivialAugment :cite:`muller2021trivialaugment` augmentation strategies.
  
  Args:
      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(TrivialAugment())
      >>> aug(in_tensor).shape
      torch.Size([5, 3, 30, 30])
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code: def __init__(
      self,
      policy: Optional[List[SUBPLOLICY_CONFIG]] = None,
      transformation_matrix_mode: str = "silent",
  ) -> None:
      if policy is None:
          _policy = default_policy
      else:
          _policy = policy
  
      super().__init__(
          _policy, transformation_matrix_mode=transformation_matrix_mode
      )
      selection_weights = torch.tensor([1.0 / len(self)] * len(self))
      self.rand_selector = Categorical(selection_weights)
  
file_path: kornia/kornia/augmentation/auto/trivial_augment/trivial_augment.py
imports: from typing import Iterator, List, Optional, Tuple
  import torch
  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.auto.rand_augment import ops
  from kornia.augmentation.container.params import ParamItem
  from kornia.core import Module
label_desc: Use image node_id 1 to create a new TrivialAugment object from inherited base classes: PolicyAugmentBase with example: obj = TrivialAugment(policy, transformation_matrix_mode)