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 forward(self, x):
      return drop_path(x, self.drop_prob, self.training)
  
imports: import math
  from functools import partial
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from timm.models.layers import drop_path, to_2tuple, trunc_normal_
  from timm.models.registry import register_model
  from transformers import CLIPImageProcessor, CLIPVisionConfig
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