code: def _run(  # type: ignore
      self,
      x,
      default_float=None,
      default_int64=None,
      default_string=None,
      default_tensor=None,
      keys_floats=None,
      keys_int64s=None,
      keys_strings=None,
      values_floats=None,
      values_int64s=None,
      values_strings=None,
      keys_tensor=None,
      values_tensor=None,
  ):
      keys = keys_floats or keys_int64s or keys_strings or keys_tensor
      values = (
          values_floats
          or values_int64s
          or values_strings
          or values_tensor
      )
      classes = dict(zip(keys, values))
  
      if values is values_tensor:
          defval = default_tensor.item()
          otype = default_tensor.dtype
      elif values is values_floats:
          defval = default_float
          otype = np.float32
      elif values is values_int64s:
          defval = default_int64
          otype = np.int64
      elif values is values_strings:
          defval = default_string
          otype = np.str_
          if not isinstance(defval, str):
              defval = ""
      lookup_func = np.vectorize(
          lambda x: classes.get(x, defval), otypes=[otype]
      )
      output = lookup_func(x)
      if output.dtype == object:
          output = output.astype(np.str_)
      return (output,)
  
imports: import numpy
  from onnx.reference.ops.aionnxml._op_run_aionnxml import OpRunAiOnnxMl
label_desc: Use image node_id 1 for calling the LabelEncoder obj's underlying member method code with example usage: obj._run(x, default_float, default_int64, default_string, default_tensor, keys_floats, keys_int64s, keys_strings, values_floats, values_int64s, values_strings, keys_tensor, values_tensor) and returns: output