code: def drop_path(x, drop_prob=0.0, training=False):
      """
      Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
      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 ...
      """
      if drop_prob == 0.0 or not training:
          return x
      keep_prob = paddle.to_tensor(1 - drop_prob, dtype=x.dtype)
      shape = (paddle.shape(x)[0],) + (1,) * (x.ndim - 1)
      random_tensor = keep_prob + paddle.rand(shape, dtype=x.dtype)
      random_tensor = paddle.floor(random_tensor)  # binarize
      output = x.divide(keep_prob) * random_tensor
      return output
  
file_path: PaddleDetection/ppdet/modeling/backbones/transformer_utils.py
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle.nn.initializer import TruncatedNormal, Constant, Assign
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