class_docstr: Get parameters for ``perspective`` for a random perspective transform.
  
  Args:
      distortion_scale: controls the degree of distortion and ranges from 0 to 1.
  
  Returns:
      A dict of parameters to be passed for transformation.
          - src (Tensor): perspective source bounding boxes with a shape of (B, 8, 3).
          - dst (Tensor): perspective target 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 __init__(
      self, distortion_scale: Union[Tensor, float] = 0.5
  ) -> None:
      super().__init__()
      self.distortion_scale = distortion_scale
  
file_path: kornia/kornia/augmentation/random_generator/_3d/perspective.py
imports: from typing import Dict, 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 Tensor, as_tensor, stack, tensor
  from kornia.utils.helpers import _extract_device_dtype
label_desc: Use image node_id 1 to create a new PerspectiveGenerator3D object from inherited base classes: RandomGeneratorBase with example: obj = PerspectiveGenerator3D(distortion_scale)