class_docstr: Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
  Adapted from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py
  
  Args:
      drop_prob (float, optional): probability of dropping path, defaults None.
class_docstr_tok: ["Drop","paths","(","Stochastic","Depth",")","per","sample","(","when","applied","in","main","path","of","residual","blocks",")",".","Adapted","from","https",":","\/\/github.com\/rwightman\/pytorch-image-models\/blob\/master\/timm\/models\/layers\/drop.py","Args",":","drop_prob","(","float",",","optional",")",":","probability","of","dropping","path",",","defaults","None","."]
code: def forward(self, x):
      return drop_path(x, self.drop_prob, self.training)
  
imports: import math
  from typing import Callable
  import torch
  import torch.nn.functional
  from torch import Tensor
  from torch import nn
  from torch.nn.parameter import Parameter
  from colossalai.legacy.context import seed
  from colossalai.legacy.registry import LAYERS
  from colossalai.nn import init
  from colossalai.utils.device import get_current_device
  from ..utils import to_2tuple
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