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 copied from ``timm.layers.DropPath`` and modified to support both NASNet and AutoFormer,
      as well as the shape inference.
  
      Credit partially goes to `pt.darts <https://github.com/khanrc/pt.darts/blob/0.1/models/ops.py>`__.
      """
      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
  
file_path: nni/nni/nas/hub/pytorch/utils/nn.py
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