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
  
      dummy = DummyClassifier()
      dummy.fit(train_embeds, train_labels)
      log = SGDClassifier(loss="log", n_jobs=55)
      log.fit(train_embeds, train_labels)
      print("Test scores")
      print(
          f1_score(
              test_labels, log.predict(test_embeds), average="micro"
          )
      )
      print("Train scores")
      print(
          f1_score(
              train_labels, log.predict(train_embeds), average="micro"
          )
      )
      print("Random baseline")
      print(
          f1_score(
              test_labels, dummy.predict(test_embeds), 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