code: def train_df():
      df_train_binary = pd.DataFrame(
          {
              "id": ["id1", "id2", "id3", "id4"],
              "x1": [10.0, 13.0, 10.0, 13.0],
              "x2": [0, 1, 1, 0],
              "x3": [13.0, 10.0, 13.0, 10.0],
              "x4": [1, 1, 0, 1],
              "x5": [13.0, 10.0, 13.0, 10.0],
              "x6": [1, 1, 0, 1],
              "w": [2, 1, 2, 0.5],
              "target": [0, 1, 0, 1],
          }
      )
  
      df_train_binary2 = pd.DataFrame(
          {
              "id": ["id1", "id2", "id3", "id4"],
              "x1": [10.0, 13.0, 10.0, 13.0],
              "x2": [0, 1, 1, 0],
              "x3": [13.0, 10.0, 13.0, 10.0],
              "x4": [1, 1, 0, 1],
              "x5": [13.0, 10.0, 13.0, 10.0],
              "x6": [1, 1, 0, 1],
              "w": [2, 1, 2, 0.5],
              "target": [0, 1, 0, 1],
          }
      )
  
      df_train_binary3 = pd.DataFrame(
          {
              "id": ["id1", "id2", "id3", "id4"],
              "x1": [10.0, 13.0, 10.0, 13.0],
              "x2": [0, 1, 1, 0],
              "x3": [13.0, 10.0, 13.0, 10.0],
              "x4": [1, 1, 0, 1],
              "x5": [13.0, 10.0, 13.0, 10.0],
              "x6": [1, 1, 0, 1],
              "w": [2, 1, 2, 0.5],
              "target": [0, 1, 0, 1],
          }
      )
  
      return pd.concat(
          [df_train_binary, df_train_binary2, df_train_binary3]
      )
  
imports: import pandas
  import pytest
  from toolz.curried import first
  from fklearn.metrics.pd_extractors import evaluator_extractor
  from fklearn.training.classification import logistic_classification_learner
  from fklearn.tuning.utils import get_used_features
  from fklearn.tuning.selectors import feature_importance_backward_selection, poor_man_boruta_selection, backward_subset_feature_selection
  from fklearn.validation.evaluators import roc_auc_evaluator
  from fklearn.validation.splitters import k_fold_splitter
  from tests import LOGS, PARALLEL_LOGS
label_desc: Use image node_id 5 for calling a global function with example usage: train_df() and returns: pd