code: def test_remove_multicollinearity():
      """Assert that one of two collinear features are dropped."""
      data = pycaret.datasets.get_data("juice")
      data["Id 2"] = list(range(len(data)))  # Correlated with Id
      pc = pycaret.classification.setup(
          data=data,
          target="STORE",
          remove_multicollinearity=True,
          multicollinearity_threshold=0.9999,
      )
  
      X, _ = pc.pipeline.transform(pc.X, pc.y)
      assert "Id" in X and "Id 2" not in X
  
imports: import io
  import numpy
  import pandas
  import pytest
  from imblearn.over_sampling import ADASYN
  from scipy.sparse import csr_matrix
  from sklearn.decomposition import PCA
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import StandardScaler
  import pycaret.classification
  import pycaret.datasets
  import pycaret.regression
label_desc: Use image node_id 32 for calling a global function with example usage: test_remove_multicollinearity() without return types