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, self.scale_by_keep
      )
  
file_path: pytorch-image-models/timm/layers/drop.py
imports: import torch
  import torch.nn
  import torch.nn.functional
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