diff --git a/examples/plot_kcustom_converter_wrapper.py b/examples/plot_kcustom_converter_wrapper.py
index cbab3577..a1752066 100644
--- a/examples/plot_kcustom_converter_wrapper.py
+++ b/examples/plot_kcustom_converter_wrapper.py
@@ -38,7 +38,7 @@
 from sklearn.decomposition import PCA
 from skl2onnx import update_registered_converter
 from skl2onnx.algebra.onnx_ops import OnnxIdentity
-from skl2onnx.algebra.onnx_operator import OnnxSubOperator
+from skl2onnx.algebra.onnx_operator import OnnxSubEstimator
 from skl2onnx import to_onnx
 
 
@@ -146,7 +146,7 @@ def decorrelate_transformer_converter(scope, operator, container):
 
     # We tell in ONNX language how to compute the unique output.
     # op_version=opv tells which opset is requested
-    subop = OnnxSubOperator(op.pca_, X, op_version=opv)
+    subop = OnnxSubEstimator(op.pca_, X, op_version=opv)
     Y = OnnxIdentity(subop, op_version=opv, output_names=out[:1])
     Y.add_to(scope, container)
 
diff --git a/examples/plot_pextend_python_runtime.py b/examples/plot_pextend_python_runtime.py
index 7bfd0527..de39acc4 100644
--- a/examples/plot_pextend_python_runtime.py
+++ b/examples/plot_pextend_python_runtime.py
@@ -165,7 +165,7 @@ class OnnxEig(OnnxOperator):
     """
 
     since_version = 1  # last changed in this version
-    expected_inputs = ['X']  # imput names
+    expected_inputs = [('X', 'T')]  # (imput names, type), T means any type
     expected_outputs = ['EigenValues', 'EigenVectors']  # output names
     input_range = [1, 1]  # only one input is allowed
     output_range = [1, 2]  # 1 or 2 outputs are produced