class_docstr: _AugmentationBase base class for customized augmentation implementations.
  
  Advanced augmentation base class with the functionality of transformation matrix calculations.
  
  Args:
      p: probability for applying an augmentation. This param controls the augmentation probabilities
        element-wise for a batch.
      p_batch: probability for applying an augmentation to a batch. This param controls the augmentation
        probabilities batch-wise.
      same_on_batch: apply the same transformation across the batch.
      keepdim: whether to keep the output shape the same as input ``True`` or broadcast it
        to the batch form ``False``.
class_docstr_tok: ["_AugmentationBase","base","class","for","customized","augmentation","implementations",".","Advanced","augmentation","base","class","with","the","functionality","of","transformation","matrix","calculations",".","Args",":","p",":","probability","for","applying","an","augmentation",".","This","param","controls","the","augmentation","probabilities","element-wise","for","a","batch",".","p_batch",":","probability","for","applying","an","augmentation","to","a","batch",".","This","param","controls","the","augmentation","probabilities","batch-wise",".","same_on_batch",":","apply","the","same","transformation","across","the","batch",".","keepdim",":","whether","to","keep","the","output","shape","the","same","as","input","``","True","``","or","broadcast","it","to","the","batch","form","``","False","``","."]
code: def apply_non_transform(
      self,
      input: Tensor,
      params: Dict[str, Tensor],
      flags: Dict[str, Any],
      transform: Optional[Tensor] = None,
  ) -> Tensor:
      # apply additional transform for the images that are skipped from transformation
      # where batch_prob == False.
      return input
  
imports: from enum import Enum
  from typing import Any, Callable, Dict, Optional, Tuple, Union
  import torch
  from torch.distributions import Bernoulli, Distribution, RelaxedBernoulli
  from kornia.augmentation.random_generator import RandomGeneratorBase
  from kornia.augmentation.utils import _adapted_rsampling, _adapted_sampling, _transform_output_shape, override_parameters
  from kornia.core import Module, Tensor, tensor, zeros
  from kornia.geometry.boxes import Boxes
  from kornia.geometry.keypoints import Keypoints
  from kornia.utils import is_autocast_enabled
label_desc: Use image node_id 2 for calling the _AugmentationBase obj's underlying member method code with example usage: obj.apply_non_transform(input, params, flags, transform) and returns: input