var session = new InferenceSession("MobileNetV2.onnx"); //the model
            var resizedImage = new Bitmap(bmp, new Size(299, 299)); //bmp is the bitmap of the image 
            var inputTensor = new DenseTensor<float>(new[] { 1, 3, 299, 299 });    // A tensor with Batch size = 1, Channels = 3, Height = 299, Width = 299
            //the batch size has to be checked
            //filling the tensors 
            for (int y = 0; y < resizedImage.Height; y++)
            {
                for (int x = 0; x < resizedImage.Width; x++)
                {
                    // grayscale value (R=G=B)
                    var pixel = resizedImage.GetPixel(x, y).R / 255.0f;
                    inputTensor[0, 0, y, x] = pixel; // Channel 1
                    inputTensor[0, 1, y, x] = pixel; // Channel 2
                    inputTensor[0, 2, y, x] = pixel; // Channel 3

                }
            }
            var inputName = session.InputMetadata.Keys.First();
            var inputs = new List<NamedOnnxValue>
            {
                NamedOnnxValue.CreateFromTensor(inputName, inputTensor)
            };

            // running the model and getting the output
            var results = session.Run(inputs);
            
            var outputName = session.OutputMetadata.Keys.First();
            var prediction = results.First(x => x.Name == outputName).AsEnumerable<float>().ToArray();

            // prediction from the model
            MessageBox.Show($"Prediction: {prediction[0]}", "Prediction Result", MessageBoxButtons.OK, MessageBoxIcon.Information);