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)
class_docstr_tok: ["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",")"]
code: def _check_consistency(
      self, *augmentations: AugmentationSequential
  ) -> bool:
      for i, aug in enumerate(augmentations):
          if not isinstance(aug, AugmentationSequential):
              raise ValueError(
                  f"Please wrap your augmentations[`{i}`] with `AugmentationSequentials`."
              )
          if (
              self.strict
              and i != 0
              and aug.data_keys != augmentations[i - 1].data_keys
          ):
              raise RuntimeError(
                  f"Different `data_keys` between {i - 1} and {i} elements, "
                  f"got {aug.data_keys} and {augmentations[i - 1].data_keys}."
              )
      return True
  
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 2 for calling the ManyToOneAugmentationDispather obj's underlying member method code with example usage: obj._check_consistency() and returns: True