code: def util_generate_dataframe_and_expected():
      np.random.seed(42)
      df = pd.DataFrame(
          np.random.randn(100, 3), columns=["x1", "x2", "x3"]
      )
      df["x4"] = df["x1"] * 0.5 + df["x2"]
      df["label"] = df["x2"] + 0.1 * df["x1"]
      df["x5"] = df["label"].apply(lambda x: "v1" if x < 0 else "v2")
  
      return df, {
          "x2": close_to(0.84, 0.01),
          "x4": close_to(0.53, 0.01),
          "x5": close_to(0.42, 0.01),
          "x1": close_to(0, 0.01),
          "x3": close_to(0, 0.01),
      }
  
imports: import numpy
  import pandas
  from hamcrest import assert_that, calling, close_to, equal_to, greater_than, has_entries, has_length, raises
  from deepchecks.core.errors import DatasetValidationError, DeepchecksNotSupportedError, DeepchecksValueError
  from deepchecks.tabular.checks.data_integrity import FeatureLabelCorrelation
  from deepchecks.tabular.checks.train_test_validation import FeatureLabelCorrelationChange
  from deepchecks.tabular.dataset import Dataset
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 1 for calling a global function with example usage: util_generate_dataframe_and_expected() and returns: df,  dict