code: def drop_path(x, drop_prob: float = 0.0, training: bool = False):
      """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 = keep_prob + torch.rand(
          shape, dtype=x.dtype, device=x.device
      )
      random_tensor.floor_()  # binarize
      output = x.div(keep_prob) * random_tensor
      return output
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 for calling a global function with example usage: drop_path(x, drop_prob, training) and returns: output, x