code: def __init__(self, input_container):
      self.onnxconverter_topology = ONNXTopology(input_container)
  
      # Declare an object to provide variables' and operators' naming mechanism.
      # One global scope is enough for parsing Hummingbird's supported input models.
      self.scope = self.onnxconverter_topology.declare_scope("__root__")
  
imports: import numpy
  import os
  from packaging.version import Version, parse
  import torch
  from uuid import uuid4
  from onnxconverter_common.registration import get_converter
  from onnxconverter_common.topology import Topology
  import onnx
  from hummingbird.ml.containers import PyTorchSklearnContainerRegression, PyTorchSklearnContainerClassification, PyTorchSklearnContainerTransformer, PyTorchSklearnContainerAnomalyDetection, TorchScriptSklearnContainerRegression, TorchScriptSklearnContainerClassification, TorchScriptSklearnContainerTransformer, TorchScriptSklearnContainerAnomalyDetection, ONNXSklearnContainerRegression, ONNXSklearnContainerClassification, ONNXSklearnContainerTransformer, ONNXSklearnContainerAnomalyDetection, TVMSklearnContainerRegression, TVMSklearnContainerClassification, TVMSklearnContainerTransformer, TVMSklearnContainerAnomalyDetection, BatchContainer
  from hummingbird.ml._utils import pandas_installed, tvm_installed, get_device, from_strings_to_ints
  from hummingbird.ml._executor import Executor
  from hummingbird.ml.exceptions import MissingConverter
  from hummingbird.ml.operator_converters import constants
label_desc: Use image node_id 1 to create a new Topology object  with example: obj = Topology(input_container)