from __future__ import annotations

import torch.nn as nn


class FullyConnectedModel(nn.Module):
    def __init__(self, num_out_features: int):
        super().__init__()
        # define layers
        self.fc1 = nn.Linear(in_features=16*16*512, out_features=512)
        self.af1 = nn.ReLU()
        self.fc2 = nn.Linear(in_features=512, out_features=128)
        self.af2 = nn.ReLU()
        self.fc3 = nn.Linear(in_features=128, out_features=32)
        self.af3 = nn.ReLU()
        self.fc4 = nn.Linear(in_features=32, out_features=num_out_features)

    def forward(self, x):
        x = self.fc1(x)
        x = self.af1(x)
        x = self.fc2(x)
        x = self.af2(x)
        x = self.fc3(x)
        x = self.af3(x)
        x = self.fc4(x)
        return x