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 forward(self, x):
      return drop_path(x, self.drop_prob, self.training)
  
imports: from torch import nn
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