class_docstr: Drop paths (Stochastic Depth) per sample  (when applied in main path of
  residual blocks).
  
  Args:
      drop_prob (float): Drop rate for paths of model. Dropout rate has
          to be between 0 and 1. Default: 0.
class_docstr_tok: ["Drop","paths","(","Stochastic","Depth",")","per","sample","(","when","applied","in","main","path","of","residual","blocks",")",".","Args",":","drop_prob","(","float",")",":","Drop","rate","for","paths","of","model",".","Dropout","rate","has","to","be","between","0","and","1",".","Default",":","0","."]
code: def __init__(self, drop_prob=0.0):
      super(DropPath, self).__init__()
      self.drop_prob = drop_prob
      self.keep_prob = 1 - drop_prob
  
imports: import torch
  from torch import nn
label_desc: Use image node_id 1 to create a new DropPath object from inherited base classes: nn with example: obj = DropPath(drop_prob)