class_docstr: SequentialBase for creating kornia modulized processing pipeline.
  
  Args:
      *args : a list of kornia augmentation and image operation modules.
      same_on_batch: apply the same transformation across the batch.
          If None, it will not overwrite the function-wise settings.
      return_transform: if ``True`` return the matrix describing the transformation
          applied to each. If None, it will not overwrite the function-wise settings.
      keepdim: whether to keep the output shape the same as input (True) or broadcast it
          to the batch form (False). If None, it will not overwrite the function-wise settings.
class_docstr_tok: ["SequentialBase","for","creating","kornia","modulized","processing","pipeline",".","Args",":","*","args",":","a","list","of","kornia","augmentation","and","image","operation","modules",".","same_on_batch",":","apply","the","same","transformation","across","the","batch",".","If","None",",","it","will","not","overwrite","the","function-wise","settings",".","return_transform",":","if","``","True","``","return","the","matrix","describing","the","transformation","applied","to","each",".","If","None",",","it","will","not","overwrite","the","function-wise","settings",".","keepdim",":","whether","to","keep","the","output","shape","the","same","as","input","(","True",")","or","broadcast","it","to","the","batch","form","(","False",")",".","If","None",",","it","will","not","overwrite","the","function-wise","settings","."]
code: def same_on_batch(self) -> Optional[bool]:
      return self._same_on_batch
  
imports: from collections import OrderedDict
  from itertools import zip_longest
  from typing import Any, Dict, Iterator, List, Optional, Tuple
  import torch
  from torch import nn
  import kornia.augmentation
  from kornia.augmentation.base import _AugmentationBase
  from kornia.core import Module, Tensor
  from kornia.geometry.boxes import Boxes
  from kornia.geometry.keypoints import Keypoints
  from .ops import BoxSequentialOps, InputSequentialOps, KeypointSequentialOps, MaskSequentialOps
  from .params import ParamItem
label_desc: Use image node_id 3 for calling the SequentialBase obj's underlying member method code with example usage: obj.same_on_batch() and returns: self