class_docstr: Dispatches different augmentations to a single input and returns a list.
  
  Same `datakeys` must be applied across different augmentations. By default, with input
  (image, mask), the augmentations must not mess it as (mask, image) to avoid unexpected
  errors. This check can be cancelled with `strict=False` if needed.
  
  Args:
      augmentations: a list or a sequence of kornia AugmentationSequential modules.
  
  Examples:
      >>> import torch
      >>> input = torch.randn(2, 3, 5, 6)
      >>> mask = torch.ones(2, 3, 5, 6)
      >>> aug_list = ManyToOneAugmentationDispather(
      ...     AugmentationSequential(
      ...         kornia.augmentation.ColorJiggle(0.1, 0.1, 0.1, 0.1, p=1.0),
      ...         kornia.augmentation.RandomAffine(360, p=1.0),
      ...         data_keys=["input", "mask",],
      ...     ),
      ...     AugmentationSequential(
      ...         kornia.augmentation.ColorJiggle(0.1, 0.1, 0.1, 0.1, p=1.0),
      ...         kornia.augmentation.RandomAffine(360, p=1.0),
      ...         data_keys=["input", "mask",],
      ...     )
      ... )
      >>> output = aug_list(input, mask)
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code: def __init__(
      self, *augmentations: AugmentationSequential, strict: bool = True
  ) -> None:
      super().__init__()
      self.strict = strict
      self._check_consistency(*augmentations)
      self.augmentations = augmentations
  
file_path: kornia/kornia/augmentation/container/dispatcher.py
imports: from typing import List, Tuple, Union
  from torch import Tensor, nn
  from .augment import AugmentationSequential
label_desc: Use image node_id 1 to create a new ManyToOneAugmentationDispather object from inherited base classes: nn with example: obj = ManyToOneAugmentationDispather()