code: def _build_sparkml_operator_list():
      """
      List all supported SparkML operators.
      """
      if sparkml_installed():
          from pyspark.ml.classification import LogisticRegressionModel
          from pyspark.ml.feature import Bucketizer, VectorAssembler
  
          supported_ops = [
              # Featurizers
              Bucketizer,
              VectorAssembler,
              # Linear Models
              LogisticRegressionModel,
          ]
  
          return supported_ops
  
      return []
  
file_path: hummingbird/hummingbird/ml/supported.py
imports: from collections import defaultdict
  from .exceptions import MissingConverter
  from ._utils import torch_installed, sklearn_installed, lightgbm_installed, xgboost_installed, onnx_runtime_installed, tvm_installed, sparkml_installed, prophet_installed
label_desc: Use image node_id 2 for calling a global function with example usage: _build_sparkml_operator_list() and returns: list, supported_ops