class_docstr: Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
      
code: def forward(self, x):
      return drop_path(x, self.drop_prob, self.training)
  
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 2 for calling the DropPath obj's underlying member method code with example usage: obj.forward(x) and returns: drop_path