Please help make this code work. I'm trying to run it in Visual Studio 2022. Thanks.

using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using Numpy;
using Python.Runtime;
using Keras;
using Keras.Layers;
using Keras.Models;
using Keras.Optimizers;
using Keras.losses;

namespace SQLInjectionDetection
{
class Program
{
static void Main(string[] args)
{
// Load CSV file
var trainData = File.ReadAllLines("tokens.csv")
.Select(l => l.Split(','))
.Select(s => new { Token = s[0], Label = int.Parse(s[1]) })
.ToList();

        // Shuffle trainData
        var random = new Random();
        trainData = trainData.OrderBy(d => random.Next()).ToList();

        // Split trainData into training and validation sets
        var splitIndex = (int)(trainData.Count \* 0.8);
        var trainDataSubset = trainData.Take(splitIndex).ToList();
        var testDataSubset = trainData.Skip(splitIndex).ToList();

        // Define vocabulary and tokenize trainData
        var vocabulary = new HashSet(trainDataSubset.SelectMany(d => d.Token).Distinct());
        var tokenToIndex = vocabulary.Select((c, i) => new { Token = c, Index = i }).ToDictionary(t => t.Token, t => t.Index);
        var maxSequenceLength = trainDataSubset.Max(d => d.Token.Length);
        var trainTokenized = Tokenize(trainDataSubset, tokenToIndex, maxSequenceLength);
        var testTokenized = Tokenize(testDataSubset, tokenToIndex, maxSequenceLength);

        // Build RNN model
        using (Py.GIL())
        {
            dynamic keras = Py.Import("keras");
            dynamic np = Py.Import("numpy");
            var input = new Input(shape: 1000);
            var embedding = new Embedding(vocabulary.Count, 32).Apply(input);
            var lstm = new LSTM(32).Apply(embedding);
            var output = new Dense(1, activation: keras.activations.sigmoid).Apply(lstm);
            var model = new Model(inputs: input, outputs: output);
            model.Compile(optimizer: new Adam(), loss: new BinaryCrossentropy(), metrics: new\[\] { "accuracy" });

            // Train model
            var trainX = trainTokenized.Item1;
            var trainY = trainTokenized.Item2;
            var testX = testTokenized.Item1;