code: def run_regression(
      train_embeds, train_labels, test_embeds, test_labels
  ):
      np.random.seed(1)
      from sklearn.linear_model import SGDClassifier
      from sklearn.dummy import DummyClassifier
      from sklearn.metrics import f1_score
      from sklearn.multioutput import MultiOutputClassifier
  
      dummy = MultiOutputClassifier(DummyClassifier())
      dummy.fit(train_embeds, train_labels)
      log = MultiOutputClassifier(SGDClassifier(loss="log"), n_jobs=10)
      log.fit(train_embeds, train_labels)
  
      f1 = 0
      for i in range(test_labels.shape[1]):
          print(
              "F1 score",
              f1_score(
                  test_labels[:, i],
                  log.predict(test_embeds)[:, i],
                  average="micro",
              ),
          )
      for i in range(test_labels.shape[1]):
          print(
              "Random baseline F1 score",
              f1_score(
                  test_labels[:, i],
                  dummy.predict(test_embeds)[:, i],
                  average="micro",
              ),
          )
  
imports: from __future__ import print_function
  import json
  import numpy
  from networkx.readwrite import json_graph
  from argparse import ArgumentParser
label_desc: Use image node_id 1 for calling a global function with example usage: run_regression(train_embeds, train_labels, test_embeds, test_labels) without return types