class_docstr: Stochastic Depth module.
  
  It performs batch-wise dropping rather than sample-wise. In libraries like
  `timm`, it's similar to `DropPath` layers that drops residual paths
  sample-wise.
  
  References:
  - https://github.com/rwightman/pytorch-image-models
  
  Args:
    drop_path_rate (float): Probability of dropping paths. Should be within
      [0, 1].
  
  Returns:
    Tensor either with the residual path dropped or kept.
class_docstr_tok: ["Stochastic","Depth","module",".","It","performs","batch-wise","dropping","rather","than","sample-wise",".","In","libraries","like","`","timm","`",",","it","'s","similar","to","`","DropPath","`","layers","that","drops","residual","paths","sample-wise",".","References",":","-","https",":","\/\/github.com\/rwightman\/pytorch-image-models","Args",":","drop_path_rate","(","float",")",":","Probability","of","dropping","paths",".","Should","be","within","[","0",",","1","]",".","Returns",":","Tensor","either","with","the","residual","path","dropped","or","kept","."]
code: def __init__(self, drop_path_rate, **kwargs):
      super().__init__(**kwargs)
      self.drop_path_rate = drop_path_rate
  
imports: import numpy
  from keras import backend
  from keras import initializers
  from keras import layers
  from keras import ops
  from keras import random
  from keras.api_export import keras_export
  from keras.applications import imagenet_utils
  from keras.layers.layer import Layer
  from keras.models import Functional
  from keras.models import Sequential
  from keras.ops import operation_utils
  from keras.utils import file_utils
label_desc: Use image node_id 1 to create a new StochasticDepth object from inherited base classes: Layer with example: obj = StochasticDepth(drop_path_rate)