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 __init__(self, drop_prob=None):
      super(DropPath, self).__init__()
      self.drop_prob = drop_prob
  
imports: import logging
  import math
  import warnings
  from enum import Enum
  from typing import Dict, List, Tuple
  import torch
  import torch.distributed
  import torch.nn
  import torch.nn.functional
  from torch.nn.modules.utils import _ntuple
  from torch.utils.checkpoint import checkpoint
  from vissl.data.collators.collator_helper import MultiDimensionalTensor
  from vissl.utils.activation_checkpointing import checkpoint_trunk
  from vissl.utils.env import get_machine_local_and_dist_rank
  from vissl.utils.misc import is_apex_available
label_desc: Use image node_id 1 to create a new DropPath object from inherited base classes: nn with example: obj = DropPath(drop_prob)