class_docstr: This layer, during training, randomly zeroes some of the elements of the 4D 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, C, H, W)` where :math:`N` is the batch size, :math:`C` is the input channels,
          :math:`H` is the input tensor height, and :math:`W` is the input tensor width
      - Output: same as the input
code: def __init__(self, p: float = 0.5, inplace: bool = False):
      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 Dropout2d object from inherited base classes: nn with example: obj = Dropout2d(p, inplace)