code: def __init__(
      self,
      contrast: Tuple[float, float] = (0.5, 1.5),
      saturation: Tuple[float, float] = (0.5, 1.5),
      hue: Tuple[float, float] = (-0.05, 0.05),
      brightness: Tuple[float, float] = (0.875, 1.125),
      p: float = 0.5,
  ):
      super().__init__()
      self._brightness = T.ColorJitter(brightness=brightness)
      self._contrast = T.ColorJitter(contrast=contrast)
      self._hue = T.ColorJitter(hue=hue)
      self._saturation = T.ColorJitter(saturation=saturation)
      self.p = p
  
imports: from typing import Dict, List, Optional, Tuple, Union
  import torch
  import torchvision
  from torch import nn, Tensor
  from torchvision import ops
  from torchvision.transforms import functional, InterpolationMode, transforms
label_desc: Use image node_id 1 to create a new RandomPhotometricDistort object from inherited base classes: nn with example: obj = RandomPhotometricDistort(contrast, saturation, hue, brightness, p)