class_docstr: Get parameters for ```erasing``` transformation for erasing transform.
  
  Args:
      scale (Tensor): range of size of the origin size cropped. Shape (2).
      ratio (Tensor): range of aspect ratio of the origin aspect ratio cropped. Shape (2).
      value (float): value to be filled in the erased area.
  
  Returns:
      A dict of parameters to be passed for transformation.
          - widths (Tensor): element-wise erasing widths with a shape of (B,).
          - heights (Tensor): element-wise erasing heights with a shape of (B,).
          - xs (Tensor): element-wise erasing x coordinates with a shape of (B,).
          - ys (Tensor): element-wise erasing y coordinates with a shape of (B,).
          - values (Tensor): element-wise filling values with a shape of (B,).
  
  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,
      scale: Union[Tensor, Tuple[float, float]] = (0.02, 0.33),
      ratio: Union[Tensor, Tuple[float, float]] = (0.3, 3.3),
      value: float = 0.0,
  ) -> None:
      super().__init__()
      self.scale = scale
      self.ratio = ratio
      self.value = value
  
file_path: kornia/kornia/augmentation/random_generator/_2d/rectangle_earase.py
imports: from typing import Dict, Tuple, Union
  import torch
  from kornia.augmentation.random_generator.base import RandomGeneratorBase, UniformDistribution
  from kornia.augmentation.utils import _adapted_rsampling, _common_param_check, _joint_range_check
  from kornia.core import Tensor, as_tensor, tensor, where
  from kornia.utils.helpers import _extract_device_dtype
label_desc: Use image node_id 1 to create a new RectangleEraseGenerator object from inherited base classes: RandomGeneratorBase with example: obj = RectangleEraseGenerator(scale, ratio, value)