import torch
import torch.nn as nn
from typing import Optional
from utils.config_model import ModelConfig

class LSTMForecastModel(nn.Module):
    def __init__(self, model_config: ModelConfig):
        super().__init__()
        self.config = model_config
        self.use_residual_skips = model_config.use_residual_skips

        # TODO: Initialize LSTM layers
        # TODO: Initialize dropout
        # TODO: Initialize output layer
        # TODO: Initialize residual connection layer if needed

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Forward pass through the LSTM network.
        
        Args:
            x: Input tensor of shape (batch_size, sequence_length, input_size)
            
        Returns:
            Predictions tensor of shape (batch_size, forecast_horizon)
        """
        # TODO: Implement forward pass with optional residual connections
        pass