class_docstr: Apply center crop on 3D volumes (5D tensor).
  
  Args:
      p: probability of applying the transformation for the whole batch.
      size (Tuple[int, int, int] or int): Desired output size (out_d, out_h, out_w) of the crop.
          If integer, out_d = out_h = out_w = size.
          If Tuple[int, int, int], out_d = size[0], out_h = size[1], out_w = size[2].
      resample: resample mode from "nearest" (0) or "bilinear" (1).
      align_corners: interpolation flag.
      keepdim: whether to keep the output shape the same as input (True) or broadcast it
        to the batch form (False).
  
  Shape:
      - Input: :math:`(C, D, H, W)` or :math:`(B, C, D, H, W)`, Optional: :math:`(B, 4, 4)`
      - Output: :math:`(B, C, out_d, out_h, out_w)`
  
  Note:
      Input tensor must be float and normalized into [0, 1] for the best differentiability support.
      Additionally, this function accepts another transformation tensor (:math:`(B, 4, 4)`), then the
      applied transformation will be merged int to the input transformation tensor and returned.
  
  Examples:
      >>> import torch
      >>> rng = torch.manual_seed(0)
      >>> inputs = torch.randn(1, 1, 2, 4, 6)
      >>> inputs
      tensor([[[[[-1.1258, -1.1524, -0.2506, -0.4339,  0.8487,  0.6920],
                 [-0.3160, -2.1152,  0.3223, -1.2633,  0.3500,  0.3081],
                 [ 0.1198,  1.2377,  1.1168, -0.2473, -1.3527, -1.6959],
                 [ 0.5667,  0.7935,  0.5988, -1.5551, -0.3414,  1.8530]],
      <BLANKLINE>
                [[ 0.7502, -0.5855, -0.1734,  0.1835,  1.3894,  1.5863],
                 [ 0.9463, -0.8437, -0.6136,  0.0316, -0.4927,  0.2484],
                 [ 0.4397,  0.1124,  0.6408,  0.4412, -0.1023,  0.7924],
                 [-0.2897,  0.0525,  0.5229,  2.3022, -1.4689, -1.5867]]]]])
      >>> aug = CenterCrop3D(2, p=1.)
      >>> aug(inputs)
      tensor([[[[[ 0.3223, -1.2633],
                 [ 1.1168, -0.2473]],
      <BLANKLINE>
                [[-0.6136,  0.0316],
                 [ 0.6408,  0.4412]]]]])
  
  To apply the exact augmenation again, you may take the advantage of the previous parameter state:
      >>> input = torch.rand(1, 3, 32, 32, 32)
      >>> aug = CenterCrop3D(24, p=1.)
      >>> (aug(input) == aug(input, params=aug._params)).all()
      tensor(True)
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code: def __init__(
      self,
      size: Union[int, Tuple[int, int, int]],
      align_corners: bool = True,
      resample: Union[str, int, Resample] = Resample.BILINEAR.name,
      p: float = 1.0,
      keepdim: bool = False,
  ) -> None:
      # same_on_batch is always True for CenterCrop
      # Since PyTorch does not support ragged tensor. So cropping function happens batch-wisely.
      super().__init__(
          p=1.0, same_on_batch=True, p_batch=p, keepdim=keepdim
      )
      if isinstance(size, tuple):
          self.size = (size[0], size[1], size[2])
      elif isinstance(size, int):
          self.size = (size, size, size)
      else:
          raise Exception(
              f"Invalid size type. Expected (int, tuple(int, int int). Got: {size}."
          )
      self.flags = {
          "align_corners": align_corners,
          "resample": Resample.get(resample),
      }
  
file_path: kornia/kornia/augmentation/_3d/geometric/center_crop.py
imports: from typing import Any, Dict, Optional, Tuple, Union, cast
  from kornia.augmentation import random_generator
  from kornia.augmentation._3d.geometric.base import GeometricAugmentationBase3D
  from kornia.constants import Resample
  from kornia.core import Tensor
  from kornia.geometry import crop_by_transform_mat3d, get_perspective_transform3d
label_desc: Use image node_id 1 to create a new CenterCrop3D object from inherited base classes: GeometricAugmentationBase3D with example: obj = CenterCrop3D(size, align_corners, resample, p, keepdim)