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: Optional[float] = None) -> None:
      super().__init__()
      self.drop_prob = drop_prob
  
imports: import collections.abc
  import math
  from typing import Dict, List, Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from ...activations import ACT2FN
  from ...modeling_outputs import BackboneOutput, BaseModelOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from ...utils.backbone_utils import BackboneMixin
  from .configuration_vitdet import VitDetConfig
label_desc: Use image node_id 1 to create a new VitDetDropPath object from inherited base classes: nn with example: obj = VitDetDropPath(drop_prob)