code: def _preprocess(self, a, cut):  # type: ignore
      if len(a.shape) == 1:
          a = a.reshape((-1, 1))
      if len(a.shape) != 2:
          raise ValueError(
              f"Every input must have 1 or 2 dimensions not {a.shape}."
          )
      if cut < a.shape[1]:
          return a[:, :cut]
      if cut > a.shape[1]:
          b = np.zeros((a.shape[0], cut), dtype=a.dtype)
          b[:, : a.shape[1]] = a
          return b
      return a
  
imports: import numpy
  from onnx.reference.ops.aionnxml._op_run_aionnxml import OpRunAiOnnxMl
label_desc: Use image node_id 1 for calling the FeatureVectorizer obj's underlying member method code with example usage: obj._preprocess(a, cut) and returns: a, a, b