code: def experiment(dataset_file, method="GraphConv", split="scaffold"):
      featurizer = "ECFP"
      if method == "GraphConv":
          featurizer = "GraphConv"
      tasks, datasets, transformers = load_dataset(
          dataset_file, featurizer=featurizer, split=split
      )
      train, val, test = datasets
  
      model = None
      if method == "GraphConv":
          model = GraphConvModel(
              len(tasks), batch_size=BATCH_SIZE, mode="regression"
          )
      elif method == "RF":
  
          def model_builder_rf(model_dir):
              sklearn_model = RandomForestRegressor(n_estimators=100)
              return dc.models.SklearnModel(sklearn_model, model_dir)
  
          model = dc.models.SingletaskToMultitask(
              tasks, model_builder_rf
          )
      elif method == "SVR":
  
          def model_builder_svr(model_dir):
              sklearn_model = svm.SVR(kernel="linear")
              return dc.models.SklearnModel(sklearn_model, model_dir)
  
          model = dc.models.SingletaskToMultitask(
              tasks, model_builder_svr
          )
  
      return model, train, val, test, transformers
  
file_path: deepchem/examples/adme/run_benchmarks.py
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import unicode_literals
  import os
  import numpy
  from sklearn.ensemble import RandomForestRegressor
  from sklearn import svm
  import tensorflow
  import deepchem
  from deepchem.models.graph_models import GraphConvModel
label_desc: Use image node_id 3 for calling a global function with example usage: experiment(dataset_file, method, split) and returns: model,  train,  val,  test,  transformers, dc, dc