code: def test_iterative_imputer_many_categories():
      """Test iterative imputer with a dataset wiht many categories"""
      # tests for pycaret/pycaret/issues/3636
      data = pycaret.datasets.get_data("titanic")
      pycaret.classification.setup(
          data,
          target="Survived",
          session_id=123,
          ignore_features=["PassengerId", "Name", "Ticket"],
          imputation_type="iterative",
          numeric_iterative_imputer="rf",
          categorical_iterative_imputer="rf",
      )
  
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 22 for calling a global function with example usage: test_iterative_imputer_many_categories() without return types