class_docstr: Abstract interface for applying and inversing transformations.
class_docstr_tok: ["Abstract","interface","for","applying","and","inversing","transformations","."]
docstr: Apply a transformation with respect to the parameters.
  
  Args:
      input: the input tensor.
      module: any torch Module but only kornia augmentation modules will count
          to apply transformations.
      param: the corresponding parameters to the module.
docstr_tok: ["Apply","a","transformation","with","respect","to","the","parameters",".","Args",":","input",":","the","input","tensor",".","module",":","any","torch","Module","but","only","kornia","augmentation","modules","will","count","to","apply","transformations",".","param",":","the","corresponding","parameters","to","the","module","."]
code: def transform(
      cls,
      input: T,
      module: Module,
      param: ParamItem,
      extra_args: Dict[str, Any] = {},
  ) -> T:
      """Apply a transformation with respect to the parameters.
  
      Args:
          input: the input tensor.
          module: any torch Module but only kornia augmentation modules will count
              to apply transformations.
          param: the corresponding parameters to the module.
      """
      raise NotImplementedError
  
imports: from abc import ABCMeta, abstractmethod
  from typing import Any, Callable, Dict, Generic, List, Optional, Type, TypeVar, Union
  from typing_extensions import ParamSpec
  import kornia.augmentation
  from kornia.augmentation.base import _AugmentationBase
  from kornia.constants import DataKey
  from kornia.core import Module, Tensor
  from kornia.geometry.boxes import Boxes
  from kornia.geometry.keypoints import Keypoints
  from .params import ParamItem
label_desc: Use image node_id 3 for calling the SequentialOpsInterface obj's underlying member method code with example usage: obj.transform(cls, input, module, param, extra_args) without return types