TypeError: forward() missing 1 required positional argument: 'events'

I appreciate this library but the documentation for models is quite lackluster and the jupyter notebook is not enough to re-use this for other, more complex, use cases.

Pretty frustrating that this error is very unclear and no documentation on how exactly inputs should be formatted or how to fix/what is going on... my input data is shaped as follows:

```
x_train.shape
>>> torch.Size([5720633, 75])

y_train.shape
>>>torch.Size([5720633, 2])
```

Getting this error when running the `lr_finder` method:
```lrfinder = model.lr_finder(x_train, y_train, batch_size, tolerance=10)```

### Full Error:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/tmp/ipykernel_517/3901793619.py in
----> 1 lrfinder = model.lr_finder(x_train, y_train, batch_size, tolerance=10)
2 _ = lr_finder.plot()

~/.local/lib/python3.7/site-packages/torchtuples/base.py in lr_finder(self, input, target, batch_size, lr_min, lr_max, lr_range, n_steps, tolerance, callbacks, verbose, num_workers, shuffle, **kwargs)
346 num_workers,
347 shuffle,
--> 348 **kwargs,
349 )
350 return lr_finder

~/.local/lib/python3.7/site-packages/pycox/models/cox.py in fit(self, input, target, batch_size, epochs, callbacks, verbose, num_workers, shuffle, metrics, val_data, val_batch_size, **kwargs)
51 return super().fit(input, target, batch_size, epochs, callbacks, verbose,
52 num_workers, shuffle, metrics, val_data, val_batch_size,
---> 53 **kwargs)
54
55 def _compute_baseline_hazards(self, input, df, max_duration, batch_size, eval_=True, num_workers=0):

~/.local/lib/python3.7/site-packages/torchtuples/base.py in fit(self, input, target, batch_size, epochs, callbacks, verbose, num_workers, shuffle, metrics, val_data, val_batch_size, **kwargs)
292 val_data, val_batch_size, shuffle=False, num_workers=num_workers, **kwargs
293 )
--> 294 log = self.fit_dataloader(dataloader, epochs, callbacks, verbose, metrics, val_dataloader)
295 return log
296

~/.local/lib/python3.7/site-packages/torchtuples/base.py in fit_dataloader(self, dataloader, epochs, callbacks, verbose, metrics, val_dataloader)
234 break
235 self.optimizer.zero_grad()
--> 236 self.batch_metrics = self.compute_metrics(data, self.metrics)
237 self.batch_loss = self.batch_metrics["loss"]
238 self.batch_loss.backward()

~/.local/lib/python3.7/site-packages/torchtuples/base.py in compute_metrics(self, data, metrics)
180 out = self.net(*input)
181 out = tuplefy(out)
--> 182 return {name: metric(*out, *target) for name, metric in metrics.items()}
183
184 def _setup_metrics(self, metrics=None):

~/.local/lib/python3.7/site-packages/torchtuples/base.py in (.0)
180 out = self.net(*input)
181 out = tuplefy(out)
--> 182 return {name: metric(*out, *target) for name, metric in metrics.items()}
183
184 def _setup_metrics(self, metrics=None):

~/.local/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1192 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1193 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1194 return forward_call(*input, **kwargs)
1195 # Do not call functions when jit is used
1196 full_backward_hooks, non_full_backward_hooks = [], []

TypeError: forward() missing 1 required positional argument: 'events'
```

Start with the model.lr_finder entry point and the torchtuples/base.py and pycox/models/cox.py stack frames shown in the report. Reproduce the call with the stated x_train and y_train shapes, then inspect the models documentation and Jupyter notebook. Done should include clear input-format guidance and an explanation of the forward() missing-events error.

Written by the indexing model from the issue text.