code: def make_le_onnx_model(
      labels: list[LT], tensor_type: type
  ) -> tuple[onnx.ModelProto, tuple[list[list[LT]], ext.NpNDArray]]:
      le = LabelEncoder()
      le.fit(labels)
  
      initial_type = [("tensor_input", tensor_type([None, 1]))]
      onnx_model = t.cast(
          onnx.ModelProto,
          convert_sklearn(le, initial_types=initial_type),
      )
      expected_input = [[labels[0]], [labels[1]]]
  
      expected_output = t.cast(
          "ext.NpNDArray", le.transform(expected_input)
      )
      expected_data = (expected_input, expected_output)
      return (onnx_model, expected_data)
  
imports: from __future__ import annotations
  import os
  import tempfile
  import typing
  from typing import TYPE_CHECKING
  import numpy
  import onnx
  import onnxruntime
  import sklearn
  import torch
  import torch.nn
  from skl2onnx import convert_sklearn
  from skl2onnx.common.data_types import FloatTensorType
  from skl2onnx.common.data_types import Int64TensorType
  from skl2onnx.common.data_types import StringTensorType
  from sklearn.datasets import load_iris
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import LabelEncoder
  import bentoml
  from .None import FrameworkTestModel
  from .None import FrameworkTestModelConfiguration
  from .None import FrameworkTestModelInput
label_desc: Use image node_id 7 for calling a global function with example usage: make_le_onnx_model(labels, tensor_type) and returns: onnx_model,  expected_data