class_docstr: Get parameters for ```crop``` transformation for crop transform.
  
  Args:
      size (tuple): Desired size of the crop operation, like (d, h, w).
          If tensor, it must be (B, 3).
      resize_to (tuple): Desired output size of the crop, like (d, h, w). If None, no resize will be performed.
  
  Returns:
      A dict of parameters to be passed for transformation.
          - src (Tensor): cropping bounding boxes with a shape of (B, 8, 3).
          - dst (Tensor): output bounding boxes with a shape (B, 8, 3).
  
  Note:
      The generated random numbers are not reproducible across different devices and dtypes. By default,
      the parameters will be generated on CPU in float32. This can be changed by calling
      ``self.set_rng_device_and_dtype(device="cuda", dtype=torch.float64)``.
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code: def __repr__(self) -> str:
      repr = f"crop_size={self.size}"
      if self.resize_to is not None:
          repr += f", resize_to={self.resize_to}"
      return repr
  
file_path: kornia/kornia/augmentation/random_generator/_3d/crop.py
imports: from typing import Dict, Optional, Tuple, Union
  import torch
  from torch.distributions import Uniform
  from kornia.augmentation.random_generator.base import RandomGeneratorBase
  from kornia.augmentation.utils import _adapted_rsampling, _common_param_check
  from kornia.core import Device, Tensor, tensor, zeros
  from kornia.geometry.bbox import bbox_generator3d
  from kornia.utils.helpers import _extract_device_dtype
label_desc: Use image node_id 2 for calling the CropGenerator3D obj's underlying member method code with example usage: obj.__repr__() and returns: repr