code: def count_categorical_split(tl_model):
      model_dump = json.loads(tl_model.dump_as_json(pretty_print=False))
      count = 0
      for tree in model_dump["trees"]:
          for node in tree["nodes"]:
              if (
                  "split_type" in node
                  and node["split_type"] == "categorical"
              ):
                  count += 1
      return count
  
imports: from cuml.testing.utils import as_type
  import cuml
  from cuml.ensemble import RandomForestClassifier
  from cuml.ensemble import RandomForestRegressor
  from cuml.common.exceptions import NotFittedError
  from cuml.internals.import_utils import has_sklearn
  from cuml.internals.import_utils import has_lightgbm, has_shap
  from cuml.explainer.tree_shap import TreeExplainer
  from hypothesis import given, settings, assume, HealthCheck, strategies
  from cuml.internals.safe_imports import gpu_only_import
  import json
  import pytest
  import treelite
  from cuml.internals.safe_imports import cpu_only_import
label_desc: Use image node_id 4 for calling a global function with example usage: count_categorical_split(tl_model) and returns: count