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 extra_repr(self) -> str:
      return "p={}".format(self.drop_prob)
  
imports: import logging
  import math
  import numpy
  import random
  from dataclasses import dataclass, field
  from typing import Optional
  from omegaconf import II
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.distributed
  from fairseq.modules import EMAModule, EMAModuleConfig
  from fairseq.dataclass import FairseqDataclass
  from fairseq.models import BaseFairseqModel, register_model
label_desc: Use image node_id 3 for calling the DropPath obj's underlying member method code with example usage: obj.extra_repr() and returns: str