class_docstr: Same as MONAI's ``list_data_collate``, except any tensors are centrally padded to match the shape of the biggest
  tensor in each dimension. This transform is useful if some of the applied transforms generate batch data of
  different sizes.
  
  This can be used on both list and dictionary data.
  Note that in the case of the dictionary data, it may add the transform information to the list of invertible transforms
  if input batch have different spatial shape, so need to call static method: `inverse` before inverting other transforms.
  
  Note that normally, a user won't explicitly use the `__call__` method. Rather this would be passed to the `DataLoader`.
  This means that `__call__` handles data as it comes out of a `DataLoader`, containing batch dimension. However, the
  `inverse` operates on dictionaries containing images of shape `C,H,W,[D]`. This asymmetry is necessary so that we can
  pass the inverse through multiprocessing.
  
  Args:
      method: padding method (see :py:class:`monai.transforms.SpatialPad`)
      mode: padding mode (see :py:class:`monai.transforms.SpatialPad`)
      kwargs: other arguments for the `np.pad` or `torch.pad` function.
          note that `np.pad` treats channel dimension as the first dimension.
code: def __init__(
      self,
      method: str = Method.SYMMETRIC,
      mode: str = PytorchPadMode.CONSTANT,
      **kwargs,
  ) -> None:
      self.method = method
      self.mode = mode
      self.kwargs = kwargs
  
file_path: MONAI/monai/transforms/croppad/batch.py
imports: from __future__ import annotations
  from collections.abc import Hashable, Mapping
  from typing import Any
  import numpy
  import torch
  from monai.data.meta_tensor import MetaTensor
  from monai.data.utils import list_data_collate
  from monai.transforms.croppad.array import CenterSpatialCrop, SpatialPad
  from monai.transforms.inverse import InvertibleTransform
  from monai.utils.enums import Method, PytorchPadMode, TraceKeys
label_desc: Use image node_id 1 to create a new PadListDataCollate object from inherited base classes: InvertibleTransform with example: obj = PadListDataCollate(method, mode)