Unable to load ONNX model for inference

Question

I have a scikit-learn pipeline for heart disease prediction. I am successful in converting the same to ONNX representation. I am unable to load this model for inference because of the following error: Fail: [ONNXRuntimeError] : 1 : FAIL : Load model from ./pipeline_xgboost.onnx failed:Node (LabelEncoder2) Op (LabelEncoder) [ShapeInferenceError] Input type is not int64 tensor but keys_int64s is set.

input_types = [
 ('age', FloatTensorType(shape=[None, 1])), #could not parse age as int 
 ('sex', Int64TensorType(shape=[None, 1])),
 ('cp', Int64TensorType(shape=[None, 1])),
 ('trestbps', Int64TensorType(shape=[None, 1])),
 ('chol', Int64TensorType(shape=[None, 1])),
 ('fbs', Int64TensorType(shape=[None, 1])),
 ('restecg', Int64TensorType(shape=[None, 1])),
 ('thalach', Int64TensorType(shape=[None, 1])),
 ('exang', Int64TensorType(shape=[None, 1])),
 ('oldpeak', FloatTensorType(shape=[None, 1])),
 ('slope', Int64TensorType(shape=[None, 1])),
 ('ca', Int64TensorType(shape=[None, 1])),
 ('thal', StringTensorType(shape=[None, 1]))]