code: def _run(  # type: ignore
      self,
      *data,
      alpha=None,
      beta=None,
      epsilon=None,
      norm_coefficient=None,
      norm_coefficient_post=None,
  ):
      if len(data) == 6:
          return self._run1(  # type: ignore
              *data,
              alpha=alpha,
              beta=beta,
              epsilon=epsilon,
              norm_coefficient=norm_coefficient,
              norm_coefficient_post=norm_coefficient_post,
          )
      n = (len(data) - 2) // 4
      xs = []
      vs = []
      hs = []
      for i in range(0, n):
          a, b, c = self._run1(  # type: ignore
              *data[:2],
              data[2 + i],
              data[2 + n + i],
              data[2 + n * 2 + i],
              data[2 + n * 3 + i],
              alpha=alpha,
              beta=beta,
              epsilon=epsilon,
              norm_coefficient=norm_coefficient,
              norm_coefficient_post=norm_coefficient_post,
          )
          xs.append(a.astype(np.float32))
          vs.append(b.astype(np.float32))
          hs.append(c.astype(np.float32))
      return tuple(xs + vs + hs)
  
imports: import numpy
  from onnx.reference.ops.aionnx_preview_training._op_run_training import OpRunTraining
label_desc: Use image node_id 1 for calling the Adam obj's underlying member method code with example usage: obj._run() and returns: tuple, self