class_docstr: This layer, during training, randomly zeroes some of the elements of the input tensor with probability `p`
  using samples from a Bernoulli distribution.
  
  Args:
      p: probability of an element to be zeroed. Default: 0.5
      inplace: If set to ``True``, will do this operation in-place. Default: ``False``
  
  Shape:
      - Input: :math:`(N, *)` where :math:`N` is the batch size
      - Output: same as the input
code: def __init__(
      self,
      p: Optional[float] = 0.5,
      inplace: Optional[bool] = False,
      *args,
      **kwargs,
  ) -> None:
      super().__init__(p=p, inplace=inplace)
  
imports: from typing import Optional
  from torch import Tensor, nn
label_desc: Use image node_id 1 to create a new Dropout object from inherited base classes: nn with example: obj = Dropout(p, inplace)