Remove tolerance + drop_rows_to_fit by default
Please take a look @NielsRogge.

I'm setting `drop_rows_to_fit=True` when the user wants that truncation. I don't think that attribute really means anything anymore given the way we handle truncation in the encoding methods, so I think it can be removed altogether.

Regarding the integration tests, I finally chose to go with a per-test tolerance instead of a relative tolerance, as the TAPAS model can output very large negative numbers; for example for the `test_inference_question_answering_head_conversational` test:

```py
        expected_tensor = torch.tensor(
            [
                [
                    -9997.22461,
                    -9997.22461,
                    -9997.22461,
                    -9997.22461,
                    -9997.22461,
                    -9997.22461,
                    -9997.22461,
                    -9997.22461,
                    -9997.22461,
                    -16.2628059,
                    -10004.082,
                    15.4330549,
                    15.4330549,
                    15.4330549,
                    -9990.42,
                    -16.3270779,
                    -16.3270779,
                    -16.3270779,
                    -16.3270779,
                    -16.3270779,
                    -10004.8506,
                ]
            ],
            device=torch_device,
        )
```

I think in these cases it's helpful to see what difference we're looking at directly in the test, and I'm not sure a relative difference would handle such ranges, but I may be mistaken here.

The tests look ok to me!<|||||>@NielsRogge removed the `drop_rows_to_fit` attribute in the last commit.