class_docstr: Stochastic drop paths per sample for residual blocks.
  Based on:
  https://github.com/rwightman/pytorch-image-models
code: def forward(self, x):
      return self.drop_path(
          x, self.drop_prob, self.training, self.scale_by_keep
      )
  
imports: from __future__ import annotations
  import torch.nn
label_desc: Use image node_id 3 for calling the DropPath obj's underlying member method code with example usage: obj.forward(x) and returns: self