code: def generate_sklearn_onnx_model():
      iris = datasets.load_iris()
      data = pd.DataFrame(
          data=np.c_[iris["data"], iris["target"]],
          columns=iris["feature_names"] + ["target"],
      )
      y = data["target"]
      x = data.drop("target", axis=1)
  
      model = LogisticRegression()
      model.fit(x, y)
  
      initial_type = [("float_input", FloatTensorType([None, 4]))]
      onx = onnxmltools.convert_sklearn(
          model, initial_types=initial_type
      )
      onnx.save_model(onx, "sklearn_model.onnx")
  
file_path: mlflow/tests/resources/onnx/generate_onnx_models.py
imports: import numpy
  import onnx
  import onnxmltools
  import pandas
  import tensorflow.compat.v1
  import tf2onnx
  from skl2onnx.common.data_types import FloatTensorType
  from sklearn import datasets
  from sklearn.linear_model import LogisticRegression
label_desc: Use image node_id 3 for calling a global function with example usage: generate_sklearn_onnx_model() without return types