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().__init__()
      self.drop_prob = drop_prob
  
imports: import math
  from functools import partial
  from typing import Tuple, List
  import numpy
  import torch
  from torch import nn
  from torch.nn.init import trunc_normal_
  from einops import rearrange
  from towhee.models.layers.layers_with_relprop import GELU, Linear, Dropout, Conv2d, Conv3d, Softmax, Sigmoid, LayerNorm, Einsum, Add, Clone, MaxPool3d, IndexSelect
label_desc: Use image node_id 1 to create a new DropPath object from inherited base classes: nn with example: obj = DropPath(drop_prob)