class_docstr: BasicSequential for creating kornia modulized processing pipeline.
  
  Args:
      *args : a list of kornia augmentation and image operation modules.
class_docstr_tok: ["BasicSequential","for","creating","kornia","modulized","processing","pipeline",".","Args",":","*","args",":","a","list","of","kornia","augmentation","and","image","operation","modules","."]
code: def __init__(self, *args: Module) -> None:
      # To name the modules properly
      _args = OrderedDict()
      for idx, mod in enumerate(args):
          if not isinstance(mod, Module):
              raise NotImplementedError(
                  f"Only Module are supported at this moment. Got {mod}."
              )
          _args.update({f"{mod.__class__.__name__}_{idx}": mod})
      super().__init__(_args)
      self._params: Optional[List[ParamItem]] = None
  
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 1 to create a new BasicSequentialBase object from inherited base classes: nn with example: obj = BasicSequentialBase()