Don't skip normalization in TabNet during inference on a single row.

[dantreiman]

Should fix #2290

The problem is that TabNet gives different outputs if you predict a single row vs. if you predict a batch.

The cause is this line in normalization_modules.py:32:

        if batch_size != 1:
            return self.bn(inputs)
       return inputs

and also tabnet_modules.py:111:

        if batch_size != 1:
            # Skip batch normalization if the batch size is 1.
            features = self.batch_norm(features)  # [b_s, i_s]

[github-actions]

Unit Test Results

       6 files  ±0         6 suites  ±0   2h 38m 54s ⏱️ +47s
2 936 tests ±0  2 887 ✔️ ±0    49 💤 ±0  0 ❌ ±0 
8 808 runs  ±0  8 625 ✔️ ±0  183 💤 ±0  0 ❌ ±0 

Results for commit 82bcaf5. ± Comparison against base commit 7f39a3c.

♻️ This comment has been updated with latest results.