Using a streaming dataloader with an unbalanced dataset yields unexpected batch sizes.

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## 🐛 Bug

I have two datasets which are unbalanced, where one dataset is 1000x larger than the other. I would like to sample from two of the datasets such that the ratio of samples from each is 1:100. When doing so, the batches are of irregular size are returned during iteration.

I think there are 2 issues which this test surfaces:
1) The first batch returned by each worker is not properly sized.
2) `drop_last` does not appear to work as intended, since the last batch is not a full sized batch

I don't think this is related to #179, but it's possible

I've been attempting to fix this, but I'm not sure what the root of the issue is. I would be very appreciative if you could fix this or point me in the right direction.

Thanks!

### To Reproduce

```
@pytest.mark.skipif(sys.platform == "win32", reason="too slow in CI")
def test_unbalanced_combined_dataset_with_dataloader(tmpdir):
data_dir_1 = os.path.join(tmpdir, "data_1")
data_dir_2 = os.path.join(tmpdir, "data_2")
cache_dir_1 = os.path.join(tmpdir, "cache_dir_1")
cache_dir_2 = os.path.join(tmpdir, "cache_dir_2")

os.makedirs(data_dir_1)
os.makedirs(data_dir_2)
os.makedirs(cache_dir_1)
os.makedirs(cache_dir_2)

cache = Cache(input_dir=str(data_dir_1), chunk_size=2)

for i in range(10):
cache[i] = i

cache.done()
cache.merge()

cache = Cache(input_dir=str(data_dir_2), chunk_size=2)

for i in range(10000):
cache[i] = i + 10

cache.done()
cache.merge()

dataset1 = StreamingDataset(input_dir=Dir(cache_dir_1, data_dir_1), shuffle=True)
dataset2 = StreamingDataset(input_dir=Dir(cache_dir_2, data_dir_2), shuffle=True)
dataset = CombinedStreamingDataset(
datasets=[dataset1, dataset2], weights=[0.01, 0.99], iterate_over_all=False, seed=12345
)
dataloader = StreamingDataLoader(dataset, num_workers=3, batch_size=100, drop_last=True, persistent_workers=True, shuffle=True, prefetch_factor=2)

assert dataset1.current_epoch == 1
assert dataset2.current_epoch == 1

batches_1 = []
batch_sizes_1 = []
for batch in dataloader:
batch_sizes_1.append(batch.size(0))
batches_1.append(batch)

assert batch_sizes_1[2] == 91
assert batch_sizes_1[-1] == 40
# This will fail since the third and last index are not 100. (Above 2 assertions pass)
assert batch_sizes_1 == [100 for _ in batches_1]

```

### Expected behavior

All batch sizes should be the same.

### Additional context

This issue is independent of whether `drop_last`, `shuffle`, and `persistent_workers` are set to True or False

Start by running test_unbalanced_combined_dataset_with_dataloader, then inspect StreamingDataLoader and CombinedStreamingDataset with the multi-worker settings in the reproduction. Trace how worker batches are assembled and how drop_last is applied. Done means the reproduced iteration returns only batches of size 100, including the first and final batches.

Written by the indexing model from the issue text.