code: def _apply_transform_unimplemented(
      self: Module, *input: Any
  ) -> Tensor:
      r"""Defines the computation performed at every call.
  
      Should be overridden by all subclasses.
      """
      raise NotImplementedError(
          f'Module [{type(self).__name__}] is missing the required "apply_tranform" function'
      )
  
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 1 for calling a global function with example usage: _apply_transform_unimplemented() without return types