class_docstr: Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
code: def __init__(self, drop_prob=None):
      super(DropPath, self).__init__()
      self.drop_prob = drop_prob
  
imports: from functools import partial
  from collections import OrderedDict
  import torch
  import torch.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)