model.py

# Create a standalone file with just the model class
import torch
import torch.nn as nn

class TabularModel(nn.Module):
    def __init__(self, input_size, hidden_sizes, output_size, dropout_rate=0.2):
        super(TabularModel, self).__init__()
        
        layers = []
        prev_size = input_size
        
        for hidden_size in hidden_sizes:
            layers.extend([
                nn.Linear(prev_size, hidden_size),
                nn.BatchNorm1d(hidden_size),
                nn.ReLU(),
                nn.Dropout(dropout_rate)
            ])
            prev_size = hidden_size
        
        layers.append(nn.Linear(prev_size, output_size))
        self.model = nn.Sequential(*layers)
    
    def forward(self, x):
        return self.model(x)