code: def test_single_condition():
      estimator = RandomForestRegressor(n_estimators=2, random_state=1)
  
      estimator.fit([[1], [2]], [1, 2])
  
      assembler = RandomForestModelAssembler(estimator)
      actual = assembler.assemble()
  
      expected = ast.BinNumExpr(
          ast.BinNumExpr(
              ast.NumVal(1.0),
              ast.IfExpr(
                  ast.CompExpr(
                      ast.FeatureRef(0),
                      ast.NumVal(1.5),
                      ast.CompOpType.LTE,
                  ),
                  ast.NumVal(1.0),
                  ast.NumVal(2.0),
              ),
              ast.BinNumOpType.ADD,
          ),
          ast.NumVal(0.5),
          ast.BinNumOpType.MUL,
      )
  
      assert cmp_exprs(actual, expected)
  
imports: from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
  from m2cgen import ast
  from m2cgen.assemblers import RandomForestModelAssembler
  from tests.utils import cmp_exprs
label_desc: Use image node_id 1 for calling a global function with example usage: test_single_condition() without return types