old_contents: """Main entry point for any experiments."""
  
  import os
  import sys
  import dataset.reuters21578.parser as data
  from evaluation.mlc import clean, evaluate
  from model.mlp import MLPLearner as Learner
  
  
  if __name__ == "__main__":
      if len(sys.argv) < 2:
          print("Usage: main.py DATA_FOLDER")
          exit(1)
  
      # Resolve data folder
      data_dir = os.path.abspath(sys.argv[1])
  
      # Fetch and preprocess dataset
      train_docs, test_docs, unused_docs = data.fetch_and_parse(data_dir)
      labels = []
      for d in train_docs + test_docs:
          for l in d.labels:
              if l not in labels:
                  labels.append(l)
  
      for layers in range(3):
          # Create and train model
          model = Learner(data_dir, labels, num_layers=layers,
                          name="mlp_" + str(layers), verbose=True)
          model.train(train_docs, test_docs=test_docs)
  
new_contents: """Main entry point for any experiments."""
  
  import os
  import sys
  import dataset.reuters21578.parser as data
  from model.mlp import MLPLearner as Learner
  
  
  if __name__ == "__main__":
      if len(sys.argv) < 2:
          print("Usage: main.py DATA_FOLDER")
          exit(1)
  
      # Resolve data folder
      data_dir = os.path.abspath(sys.argv[1])
  
      # Fetch and preprocess dataset
      train_docs, test_docs, unused_docs = data.fetch_and_parse(data_dir)
      labels = []
      for d in train_docs + test_docs:
          for l in d.labels:
              if l not in labels:
                  labels.append(l)
  
      for layers in range(3):
          # Create and train model
          model = Learner(data_dir, labels, num_layers=layers,
                          name="mlp_" + str(layers), verbose=True)
          model.train(train_docs, test_docs=test_docs)
  
subject: remove unused dependencies