code: def drop_path(
      x,
      drop_prob: float = 0.0,
      training: bool = False,
      scale_by_keep: bool = True,
  ):
      """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
  
      This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
      the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
      See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
      changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
      'survival rate' as the argument.
  
      """
      if drop_prob == 0.0 or not training:
          return x
      keep_prob = 1 - drop_prob
      shape = (x.shape[0],) + (1,) * (
          x.ndim - 1
      )  # work with diff dim tensors, not just 2D ConvNets
      random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
      if keep_prob > 0.0 and scale_by_keep:
          random_tensor.div_(keep_prob)
      return x * random_tensor
  
imports: from torch import nn
label_desc: Use image node_id 1 for calling a global function with example usage: drop_path(x, drop_prob, training, scale_by_keep) and returns: unknown, x