class_docstr: Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
  "Deep Networks with Stochastic Depth", https://arxiv.org/pdf/1603.09382.pdf
class_docstr_tok: ["Drop","paths","(","Stochastic","Depth",")","per","sample","(","when","applied","in","main","path","of","residual","blocks",")",".","``","Deep","Networks","with","Stochastic","Depth","''",",","https",":","\/\/arxiv.org\/pdf\/1603.09382.pdf"]
code: def __init__(self, drop_prob=None):
      super(DropPath, self).__init__()
      self.drop_prob = drop_prob
  
imports: from collections import OrderedDict
  from functools import partial
  from typing import Callable, Optional
  import torch.nn
  import torch
  from torch import Tensor
  from utils import *
label_desc: Use image node_id 1 to create a new DropPath object from inherited base classes: nn with example: obj = DropPath(drop_prob)