Sorry, it took a bit longer.
It should work by changing the input from 1 to 74 and the output from 1 to 51.
Like this:
class SimpleCustomModel(torch.nn.Module):
    def __init__(self, num_layers=5, max_hidden_dim=512):
        
        super().__init__()
        
        layers = [torch.nn.Linear(71, 96),
                 torch.nn.ReLU()]
        
        curr_dim = 16
        for i in range(num_layers - 2):
            new_dim = min(curr_dim * 2, max_hidden_dim)
            layers += [torch.nn.Linear(curr_dim, new_dim),
                      torch.nn.ReLU()]
            curr_dim = new_dim
            
        layers.append(torch.nn.Linear(curr_dim, 51))
        
        self.model = torch.nn.Sequential(*layers)
        
    def forward(self, x):
        return self.model(x)


Note: I modified the internal structure a bit so that it should better suit your problem.

Unfortunately, I’m unable to help you with your data loading code without example files.