docs: def upload_to_metta(train_features_path, train_labels_path, test_features_path, test_labels_path, train_quarter, test_quarter, num_dimensions):
      """Store train and test matrices using metta
  
      Args:
          train_features_path (str) Path to matrix with train features
          train_labels_path (str) Path to matrix with train labels
          test_features_path (str) Path to matrix with test features
          test_labels_path (str) Path to matrix with test labels
          train_quarter (str) Quarter of train matrix
          test_quarter (str) Quarter of test matrix
          num_dimensions (int) Number of features
      """
      train_config = metta_config(train_quarter, num_dimensions)
      test_config = metta_config(test_quarter, num_dimensions)
  
      X_train = pd.read_csv(train_features_path, sep=',')
      X_train.columns = ['doc2vec_'+str(i) for i in range(X_train.shape[1])]
      #X_train['label'] = pd.Series([randint(0,23) for i in range(len(X_train))])
      Y_train = pd.read_csv(train_labels_path)
      Y_train.columns = ['onet_soc_code']
      train = pd.concat([X_train, Y_train], axis=1)
  
      X_test = pd.read_csv(test_features_path, sep=',')
      X_test.columns = ['doc2vec_'+str(i) for i in range(X_test.shape[1])]
      #X_test['label'] = pd.Series([randint(0,23) for i in range(len(X_test))])
      Y_test = pd.read_csv(test_labels_path)
      Y_test.columns = ['onet_soc_code']
      test = pd.concat([X_test, Y_test], axis=1)
      #print(train.head())
      #print(train.shape)
      #print(test.head())
      #print(test.shape)
      metta.archive_train_test(
          train_config,
          X_train,
          test_config,
          X_test,
          directory='wdi'
      )