code: def _run(self, *data, decay_factor=None, epsilon=None, norm_coefficient=None):  # type: ignore
      if len(data) == 5:
          return self._run1(  # type: ignore
              *data,
              decay_factor=decay_factor,
              epsilon=epsilon,
              norm_coefficient=norm_coefficient,
          )
      n = (len(data) - 2) // 3
      xs = []
      hs = []
      for i in range(0, n):
          a, b = self._run1(  # type: ignore
              *data[:2],
              data[2 + i],
              data[2 + n + i],
              data[2 + n * 2 + i],
              decay_factor=decay_factor,
              epsilon=epsilon,
              norm_coefficient=norm_coefficient,
          )
          xs.append(a)
          hs.append(b)
      return tuple(xs + 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 Adagrad obj's underlying member method code with example usage: obj._run() and returns: tuple, self