import torch
import torch.nn as nn

class CardiovascularRNN(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, num_classes):
        super(CardiovascularRNN, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        
        # RNN layer (using GRU for better performance over plain RNN)
        self.gru = nn.GRU(input_size, hidden_size, num_layers, batch_first=True)
        
        # Fully connected layer
        self.fc = nn.Linear(hidden_size, num_classes)
        
    def forward(self, x):
        # Initialize hidden state
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
        
        # Forward propagate GRU
        out, _ = self.gru(x, h0)
        
        # Decode the hidden state of the last time step
        out = out[:, -1, :]
        
        # Output layer
        out = self.fc(out)
        return out