var emptyData = new List<CountInput>();

        var data = context.Data.LoadFromEnumerable(emptyData);

        var pipeline = context.Transforms.ResizeImages(
                resizing: ImageResizingEstimator.ResizingKind.Fill, 
                outputColumnName: "feed_resized", 
                imageWidth: ImageSettings.imageWidth, 
                imageHeight: ImageSettings.imageHeight, 
                inputColumnName: nameof(CountInput.Image)
            )
            .Append(context.Transforms.ConvertToGrayscale(outputColumnName: "grayscale", inputColumnName: "feed_resized"))
            .Append(context.Transforms.ExtractPixels(
                outputColumnName: "input_1:0", inputColumnName: "grayscale",
                interleavePixelColors: false,
                colorsToExtract: Microsoft.ML.Transforms.Image.ImagePixelExtractingEstimator.ColorBits.Alpha,
                outputAsFloatArray: true,
                scaleImage: .00390625f))
            .Append(context.Transforms.ApplyOnnxModel(
                modelFile: _model_path, 
                outputColumnNames: new[] { "tf.concat_3", "tf.concat_5", "tf.concat_7" }, 
                inputColumnNames: new[] { "input_1:0" })
            );

        var model = pipeline.Fit(data);

        _predictionEngine = context.Model.CreatePredictionEngine<CountInput, CountPrediction>(model);

        string image_path = @"D:\ml_dotnet\tests\test.png";
        var image = (Bitmap)Image.FromFile(image_path);

        var prediction = _predictionEngine.Predict(new CountInput { Image = image });