class_docstr: Randomly zeroes 3D spatial blocks of the input tensor.
  An extension to the concept described in the paper
  `DropBlock: A regularization method for convolutional networks`_ ,
  dropping whole blocks of feature map allows to remove semantic
  information as compared to regular dropout.
  Args:
      drop_prob (float): probability of an element to be dropped.
      block_size (int): size of the block to drop
  Shape:
      - Input: `(N, C, D, H, W)`
      - Output: `(N, C, D, H, W)`
  .. _DropBlock: A regularization method for convolutional networks:
     https://arxiv.org/abs/1810.12890
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code: def __init__(self, drop_prob, block_size):
      super(DropBlock3D, self).__init__(drop_prob, block_size)
  
file_path: pytorch-toolbelt/pytorch_toolbelt/modules/dropblock.py
imports: import torch
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 1 to create a new DropBlock3D object from inherited base classes: DropBlock2D with example: obj = DropBlock3D(drop_prob, block_size)