Torch backend returns tensor of different shape and data type then input

First of all, thank you for implementing this library, it is useful to have different stain normalization algorithms with a simple pip install.

**Describe the bug**
Macenko normalizer changes the image shape and dtype after normalization,
which requires additional steps in the dataloader which is unexpected and unnecessary.

**To Reproduce**
```
H, W, C = 100, 100, 3
target = torch.rand(C, H, W).to(torch.float32)
query = torch.rand(C, H, W).to(torch.float32)
normalizer = torchstain.normalizers.MacenkoNormalizer(backend="torch")
normalizer.fit(target)
query_norm = normalizer.normalize(query)[0]
assert query_norm.shape == query.shape, f'{query_norm.shape} == {query.shape}'
assert query_norm.dtype == query.dtype, f'{query_norm.dtype} == {query.dtype}'
```

the output is

```
AssertionError: torch.Size([100, 100, 3]) == torch.Size([3, 100, 100])
AssertionError: torch.int32 == torch.float32
```

**Expected behavior**
Image before and after normalization should be the same shape and type.