code: def _private_run(
      self,
      X: np.ndarray,
      seed: Optional[int] = None,
      ratio: float = 0.5,
      training_mode: bool = False,
  ) -> Tuple[np.ndarray]:
      return _dropout(
          X,
          ratio,
          seed=seed,  # type: ignore
          return_mask=self.n_outputs == 2,
          training_mode=training_mode,
      )
  
file_path: onnx/onnx/reference/ops/op_dropout.py
imports: from typing import Optional, Tuple
  import numpy
  from numpy.random import RandomState
  from onnx.reference.op_run import OpRun
label_desc: Use image node_id 2 for calling the DropoutBase obj's underlying member method code with example usage: obj._private_run(X, seed, ratio, training_mode) and returns: _dropout