class_docstr: AugmentationBase2D base class for customized augmentation implementations.
  
  AugmentationBase2D aims at offering a generic base class for a greater level of customization.
  If the subclass contains routined matrix-based transformations, `RigidAffineAugmentationBase2D`
  might be a better fit.
  
  Args:
      p: probability for applying an augmentation. This param controls the augmentation probabilities
        element-wise for a batch.
      p_batch: probability for applying an augmentation to a batch. This param controls the augmentation
        probabilities batch-wise.
      same_on_batch: apply the same transformation across the batch.
      keepdim: whether to keep the output shape the same as input ``True`` or broadcast it to the batch
        form ``False``.
docstr: Convert any incoming (H, W), (C, H, W) and (B, C, H, W) into (B, C, H, W).
docstr_tok: ["Convert","any","incoming","(","H",",","W",")",",","(","C",",","H",",","W",")","and","(","B",",","C",",","H",",","W",")","into","(","B",",","C",",","H",",","W",")","."]
code: def transform_tensor(self, input: Tensor) -> Tensor:
      """Convert any incoming (H, W), (C, H, W) and (B, C, H, W) into (B, C, H, W)."""
      _validate_input_dtype(
          input, accepted_dtypes=[float16, float32, float64]
      )
      return _transform_input(input)
  
imports: from typing import Any, Dict, Optional
  from torch import float16, float32, float64
  from kornia.augmentation.base import _AugmentationBase
  from kornia.augmentation.utils import _transform_input, _validate_input_dtype
  from kornia.core import Tensor
  from kornia.geometry.boxes import Boxes
  from kornia.geometry.keypoints import Keypoints
  from kornia.utils import eye_like, is_autocast_enabled
label_desc: Use image node_id 2 for calling the AugmentationBase2D obj's underlying member method code with example usage: obj.transform_tensor(input) and returns: _transform_input