class_docstr: Generate random color jiter parameters for a batch of images following OpenCV.
  
  Args:
      brightness: The brightness factor to apply.
      contrast: The contrast factor to apply.
      saturation: The saturation factor to apply.
      hue: The hue factor to apply.
  
  Returns:
      A dict of parameters to be passed for transformation.
          - brightness_factor: element-wise brightness factors with a shape of (B,).
          - contrast_factor: element-wise contrast factors with a shape of (B,).
          - hue_factor: element-wise hue factors with a shape of (B,).
          - saturation_factor: element-wise saturation factors with a shape of (B,).
          - order: applying orders of the color adjustments with a shape of (4). In which,
              0 is brightness adjustment; 1 is contrast adjustment;
              2 is saturation adjustment; 3 is hue adjustment.
  
  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:
      return f"brightness={self.brightness}, contrast={self.contrast}, saturation={self.saturation}, hue={self.hue}"
  
file_path: kornia/kornia/augmentation/random_generator/_2d/color_jiggle.py
imports: from functools import partial
  from typing import Dict, List, 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, _range_bound
  from kornia.core import Tensor
  from kornia.utils.helpers import _extract_device_dtype
label_desc: Use image node_id 2 for calling the ColorJiggleGenerator obj's underlying member method code with example usage: obj.__repr__() and returns: str, self, str, self, str, self, str, self