ML.net - error when consuming ONNX in ML.net

Using ML.net in my .NET core app I'm trying to consume a KERAS LSTM model that I exported to an ONNX file. Here is my code:

using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms.Onnx;

public void getmodel(mydata[] data1)
{
    string modelPath = "C:\\MyStuff\\ONNXtest.onnx";
    MLContext mlContext = new MLContext();

    IDataView data = mlContext.Data.LoadFromEnumerable<mydata>(data1);

    OnnxScoringEstimator pipeline = mlContext.Transforms.ApplyOnnxModel(new[] { "output" }, new[] { "input" }, modelPath);

    IEnumerable<mydata> testdata = mlContext.Data.CreateEnumerable<mydata>(data, reuseRowObject: true);
    foreach (mydata row in testdata)
    {
        System.Diagnostics.Debug.WriteLine(row.myval[0]);
        System.Diagnostics.Debug.WriteLine(row.myval[1]);
        System.Diagnostics.Debug.WriteLine(row.myval[2]);
        System.Diagnostics.Debug.WriteLine(row.myval[3]);
        System.Diagnostics.Debug.WriteLine(row.myval[4]);
    }

    OnnxTransformer test = pipeline.Fit(data);

    IDataView transformedValues = test.Transform(data);

    IEnumerable<float[]> results = transformedValues.GetColumn<float[]>("output");

    double result = Convert.ToDouble(results.ElementAtOrDefault(0).GetValue(0));

}

This is how the mydata class looks like:

public class mydata
{
     [VectorType(1,5,1)]
     [ColumnName("input")]   
     public float[] myval { get; set; }
}

I want to feed 5 values in the model and looking at the "System.Diagnostics.Debug.WriteLine" output it seems like everything works and the IDataView data contains 5 values for being fed into the model. However the pipeline.Fit(data) line causes a "System.ArgumentOutOfRangeException" in Microsoft.ML.OnnxTransformer.dll error.

Here is also the code that trains and exports the LSTM in python:

regressor = Sequential()
regressor.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 1),name ='input'))
regressor.add(Dropout(0.2))
regressor.add(LSTM(units = 50, return_sequences = True))
regressor.add(Dropout(0.2))
regressor.add(LSTM(units = 50, return_sequences = True))
regressor.add(Dropout(0.2))
regressor.add(LSTM(units = 50))
regressor.add(Dropout(0.2))

regressor.add(Dense(units = 1,name ='output'))
regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')
regressor.fit(X_train, y_train, epochs = 100, batch_size = 32)

from winmltools import convert_keras
model_onnx = convert_keras(regressor, 7, name='sequential_7')
from winmltools.utils import save_model
save_model(model_onnx, 'C:\\MyStuff\\ONNXtest.onnx')

This is the summary of the network that is exported:

Model: "sequential_7"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input (LSTM)                 (None, 5, 50)             10400     
_________________________________________________________________
dropout_25 (Dropout)         (None, 5, 50)             0         
_________________________________________________________________
lstm_21 (LSTM)               (None, 5, 50)             20200     
_________________________________________________________________
dropout_26 (Dropout)         (None, 5, 50)             0         
_________________________________________________________________
lstm_22 (LSTM)               (None, 5, 50)             20200     
_________________________________________________________________
dropout_27 (Dropout)         (None, 5, 50)             0         
_________________________________________________________________
lstm_23 (LSTM)               (None, 50)                20200     
_________________________________________________________________
dropout_28 (Dropout)         (None, 50)                0         
_________________________________________________________________
output (Dense)               (None, 1)                 51        
=================================================================
Total params: 71,051
Trainable params: 71,051
Non-trainable params: 0

If I just consume the model in Python it works perfectly and a very similar setup (in fact I think it was the same code) a while ago worked perfectly in ML.net. But now I'm not even sure whether my error is on the python or on the c# side. Can anyone help me to figure out how to handle this error?