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 collections.abc import Callable
  import numpy
  import paddle
  import paddle.nn
  from paddle.nn.initializer import TruncatedNormal, Constant, Normal
  from ppdet.core.workspace import register, serializable
label_desc: Use image node_id 1 to create a new DropPath object from inherited base classes: nn with example: obj = DropPath(drop_prob)