class_docstr: Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
class_docstr_tok: ["Drop","paths","(","Stochastic","Depth",")","per","sample","(","when","applied","in","main","path","of","residual","blocks",")","."]
code: def __init__(self, drop_prob: float = 0.0):
      super(DropPath, self).__init__()
      self.drop_prob = drop_prob
  
imports: 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)