class_docstr: Randomly flip and crop an image.
  
  A PyTorch module that applies random cropping, horizontal and vertical flipping to an image,
  and returns the transformed image and a grid tensor used to map the image back to the
  original image space in an NxN grid.
  
  Args:
      grid_size:
          The number of grid cells in the output grid tensor.
      crop_size:
          The size (in pixels) of the random crops.
      crop_min_scale:
          The minimum scale factor for random resized crops.
      crop_max_scale:
          The maximum scale factor for random resized crops.
      hf_prob:
          The probability of applying horizontal flipping to the image.
      normalize:
          A dictionary containing the mean and std values for normalizing the image.
class_docstr_tok: ["Randomly","flip","and","crop","an","image",".","A","PyTorch","module","that","applies","random","cropping",",","horizontal","and","vertical","flipping","to","an","image",",","and","returns","the","transformed","image","and","a","grid","tensor","used","to","map","the","image","back","to","the","original","image","space","in","an","NxN","grid",".","Args",":","grid_size",":","The","number","of","grid","cells","in","the","output","grid","tensor",".","crop_size",":","The","size","(","in","pixels",")","of","the","random","crops",".","crop_min_scale",":","The","minimum","scale","factor","for","random","resized","crops",".","crop_max_scale",":","The","maximum","scale","factor","for","random","resized","crops",".","hf_prob",":","The","probability","of","applying","horizontal","flipping","to","the","image",".","normalize",":","A","dictionary","containing","the","mean","and","std","values","for","normalizing","the","image","."]
code: def __init__(
      self,
      grid_size: int = 7,
      crop_size: int = 224,
      crop_min_scale: float = 0.05,
      crop_max_scale: float = 0.2,
      hf_prob: float = 0.5,
      vf_prob: float = 0.5,
  ):
      super().__init__()
      self.grid_size = grid_size
      self.crop_size = crop_size
      self.crop_min_scale = crop_min_scale
      self.crop_max_scale = crop_max_scale
      self.hf_prob = hf_prob
      self.vf_prob = vf_prob
      self.resized_crop = RandomResizedCropWithLocation(
          size=self.crop_size,
          scale=(self.crop_min_scale, self.crop_max_scale),
      )
      self.horizontal_flip = RandomHorizontalFlipWithLocation(
          self.hf_prob
      )
      self.vertical_flip = RandomVerticalFlipWithLocation(self.vf_prob)
  
imports: from dataclasses import dataclass
  from typing import Tuple
  import torch
  import torchvision.transforms
  import torchvision.transforms.functional
  from PIL import Image
  from torch import nn
label_desc: Use image node_id 1 to create a new RandomResizedCropAndFlip object from inherited base classes: nn with example: obj = RandomResizedCropAndFlip(grid_size, crop_size, crop_min_scale, crop_max_scale, hf_prob, vf_prob)