This is my SVR model that I’m using. I need to add a GWO optimiser to it but am a noob. What code do I include?

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim.lr_scheduler import ReduceLROnPlateau
from scipy import stats

# Define SVR model using PyTorch
class SVRModel(nn.Module):
    def __init__(self):
        super(SVRModel, self).__init__()
        self.fc1 = nn.Linear(in_features=7, out_features=64)  # Adjust input and output features if needed 
        self.fc2 = nn.Linear(in_features=64, out_features=32) 
        self.fc3 = nn.Linear(in_features=32, out_features=1)  # Output layer

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x


# Convert data to PyTorch tensors
X_train_tensor = torch.tensor(scaled_X_train, dtype=torch.float32)
Y_train_tensor = torch.tensor(Y_train.reshape(-1, 1), dtype=torch.float32)
X_test_tensor = torch.tensor(scaled_X_test, dtype=torch.float32)

# Define hyperparameters
C = 1
epsilon = 0.1
lr = 0.01
epochs = 1000

# Instantiate SVR model
model = SVRModel()

# Define loss function and optimizer
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=lr)

# Define learning rate scheduler
scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=0.5, patience=5, verbose=True)

# Training loop
for epoch in range(epochs):
    model.train()
    optimizer.zero_grad()
    outputs = model(X_train_tensor)
    loss = criterion(outputs, Y_train_tensor)
    loss.backward()
    optimizer.step()
    
    # Step scheduler
    scheduler.step(loss)
    
    print(f'Epoch [{epoch+1}/{epochs}], Loss: {loss.item():.4f}')

# Evaluation
model.eval()
with torch.no_grad():
    Y_pred_tensor = model(X_test_tensor)
    Y_pred = Y_pred_tensor.numpy().flatten()

# Calculate correlation
corr = stats.pearsonr(Y_test, Y_pred)[0]
print(f'Correlation: {corr}')

# Create result DataFrame
result_df = pd.DataFrame({'svr_predicted': Y_pred, 'true': Y_test})
result_df['svr_error'] = abs(result_df['true'] - result_df['svr_predicted'])

I just need to improve the accuracy of my model by a little and want to try a GWO to impress my instructor. Again, I am a noobie at this. I tried to watch some YT videos to do it myself and nothing is working. If someone can just write the code for me, you’d be saving my life!