code: def forward(
      self, image: Tensor, target: Optional[Dict[str, Tensor]] = None
  ) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]:
      if isinstance(image, torch.Tensor):
          if image.ndimension() not in {2, 3}:
              raise ValueError(
                  f"image should be 2/3 dimensional. Got {image.ndimension()} dimensions."
              )
          elif image.ndimension() == 2:
              image = image.unsqueeze(0)
  
      r = torch.rand(7)
  
      if r[0] < self.p:
          image = self._brightness(image)
  
      contrast_before = r[1] < 0.5
      if contrast_before:
          if r[2] < self.p:
              image = self._contrast(image)
  
      if r[3] < self.p:
          image = self._saturation(image)
  
      if r[4] < self.p:
          image = self._hue(image)
  
      if not contrast_before:
          if r[5] < self.p:
              image = self._contrast(image)
  
      if r[6] < self.p:
          channels, _, _ = F.get_dimensions(image)
          permutation = torch.randperm(channels)
  
          is_pil = F._is_pil_image(image)
          if is_pil:
              image = F.pil_to_tensor(image)
              image = F.convert_image_dtype(image)
          image = image[..., permutation, :, :]
          if is_pil:
              image = F.to_pil_image(image)
  
      return image, target
  
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 2 for calling the RandomPhotometricDistort obj's underlying member method code with example usage: obj.forward(image, target) and returns: image,  target