code: def __init__(
      self,
      p=0.5,
      scale=(0.02, 0.33),
      ratio=(0.3, 3.3),
      value=0,
      inplace=False,
      max_erase=1,
  ):
      super().__init__(
          p=p, scale=scale, ratio=ratio, value=value, inplace=inplace
      )
      self.max_erase = max_erase
      if self.max_erase <= 0:
          raise ValueError("max_raise should be greater than 0")
  
imports: import torch
  import torchvision.transforms
  import torchvision.transforms.functional
label_desc: Use image node_id 1 to create a new RandomErasing object from inherited base class: T with example: obj = RandomErasing(p, scale, ratio, value, inplace, max_erase)