Drop incomplete batches for Ray and Pandas to prevent Batchnorm computation errors

[arnavgarg1]

This PR implements logic to drop the last batch for Pandas and Ray dataset batchers to prevent issues when there is only 1 row in a batch.

A single row in a batch causes issues when computing Batchnorm (such as in FC layers, Tabnet, etc.) because it can't compute the mean/stddev for a single sample. Additionally, Ludwig has some logic for Tabnet that has a conditional check for sample_size == 1 that we can now safely remove.

Skipping adding tests since our current test suite already has tests that use the batcher classes for model training, so if the tests pass, then this logic should work as well. I modified individual tests locally to be sure.

Note: This only drops incomplete training batches if the batch size < total number of rows in the dataset. Additionally, it will always keep incomplete training batches for validation/test sets.

To follow:

• Remove check for batch_size == 1 in Tabnet

[github-actions]

Unit Test Results

         6 files  ±  0           6 suites  ±0   3h 31m 23s ⏱️ - 14m 16s
  3 527 tests  -   5    3 456 ✔️  - 1    71 💤  -   4  0 ❌ ±0 
10 581 runs   - 15  10 353 ✔️ ±0  228 💤  - 15  0 ❌ ±0 

Results for commit bd759ff. ± Comparison against base commit 8f5d013.

♻️ This comment has been updated with latest results.

[ShreyaR]

lgtm!