code: def _build_registered_operators() -> (
      Dict[str, Dict[Union[int, None], OpRunAiOnnxMl]]
  ):
      return build_registered_operators_any_domain(globals().copy())
  
imports: import textwrap
  from typing import Any, Dict
  from typing import Optional
  from typing import Union
  from onnx.reference.op_run import OpFunction
  from onnx.reference.ops._helpers import build_registered_operators_any_domain
  from onnx.reference.ops.aionnxml._op_run_aionnxml import OpRunAiOnnxMl
  from onnx.reference.ops.aionnxml.op_array_feature_extractor import ArrayFeatureExtractor
  from onnx.reference.ops.aionnxml.op_binarizer import Binarizer
  from onnx.reference.ops.aionnxml.op_dict_vectorizer import DictVectorizer
  from onnx.reference.ops.aionnxml.op_feature_vectorizer import FeatureVectorizer
  from onnx.reference.ops.aionnxml.op_imputer import Imputer
  from onnx.reference.ops.aionnxml.op_label_encoder import LabelEncoder
  from onnx.reference.ops.aionnxml.op_linear_classifier import LinearClassifier
  from onnx.reference.ops.aionnxml.op_linear_regressor import LinearRegressor
  from onnx.reference.ops.aionnxml.op_normalizer import Normalizer
  from onnx.reference.ops.aionnxml.op_one_hot_encoder import OneHotEncoder
  from onnx.reference.ops.aionnxml.op_scaler import Scaler
  from onnx.reference.ops.aionnxml.op_svm_classifier import SVMClassifier
  from onnx.reference.ops.aionnxml.op_svm_regressor import SVMRegressor
  from onnx.reference.ops.aionnxml.op_tree_ensemble_classifier import TreeEnsembleClassifier
  from onnx.reference.ops.aionnxml.op_tree_ensemble_regressor import TreeEnsembleRegressor
label_desc: Use image node_id 1 for calling a global function with example usage: _build_registered_operators() and returns: build_registered_operators_any_domain