class_docstr: Randomly distorts the image or video as used in `SSD: Single Shot
  MultiBox Detector <https://arxiv.org/abs/1512.02325>`_.
  
  This transform relies on :class:`~torchvision.transforms.v2.ColorJitter`
  under the hood to adjust the contrast, saturation, hue, brightness, and also
  randomly permutes channels.
  
  Args:
      brightness (tuple of float (min, max), optional): How much to jitter brightness.
          brightness_factor is chosen uniformly from [min, max]. Should be non negative numbers.
      contrast (tuple of float (min, max), optional): How much to jitter contrast.
          contrast_factor is chosen uniformly from [min, max]. Should be non-negative numbers.
      saturation (tuple of float (min, max), optional): How much to jitter saturation.
          saturation_factor is chosen uniformly from [min, max]. Should be non negative numbers.
      hue (tuple of float (min, max), optional): How much to jitter hue.
          hue_factor is chosen uniformly from [min, max].  Should have -0.5 <= min <= max <= 0.5.
          To jitter hue, the pixel values of the input image has to be non-negative for conversion to HSV space;
          thus it does not work if you normalize your image to an interval with negative values,
          or use an interpolation that generates negative values before using this function.
      p (float, optional) probability each distortion operation (contrast, saturation, ...) to be applied.
          Default is 0.5.
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code: def __init__(
      self,
      brightness: Tuple[float, float] = (0.875, 1.125),
      contrast: Tuple[float, float] = (0.5, 1.5),
      saturation: Tuple[float, float] = (0.5, 1.5),
      hue: Tuple[float, float] = (-0.05, 0.05),
      p: float = 0.5,
  ):
      super().__init__()
      self.brightness = brightness
      self.contrast = contrast
      self.hue = hue
      self.saturation = saturation
      self.p = p
  
imports: import collections.abc
  from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
  import torch
  from torchvision import transforms
  from torchvision.transforms.v2 import functional, Transform
  from ._transform import _RandomApplyTransform
  from ._utils import query_chw
label_desc: Use image node_id 1 to create a new RandomPhotometricDistort object from inherited base classes: Transform with example: obj = RandomPhotometricDistort(brightness, contrast, saturation, hue, p)