class_docstr: Dispatches different augmentations to different inputs element-wisely.
  
  Args:
      augmentations: a list or a sequence of kornia AugmentationSequential modules.
  
  Examples:
      >>> import torch
      >>> input_1, input_2 = torch.randn(2, 3, 5, 6), torch.randn(2, 3, 5, 6)
      >>> mask_1, mask_2 = torch.ones(2, 3, 5, 6), torch.ones(2, 3, 5, 6)
      >>> aug_list = ManyToManyAugmentationDispather(
      ...     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_1, mask_1), (input_2, mask_2))
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code: def forward(
      self, *input: Union[List[Tensor], List[Tuple[Tensor]]]
  ) -> Union[List[Tensor], List[Tuple[Tensor]]]:
      return [aug(*inp) for inp, aug in zip(input, self.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 3 for calling the ManyToManyAugmentationDispather obj's underlying member method code with example usage: obj.forward() and returns: unknown