Inconsistent `from_logits` parameter across loss functions

`DiceLoss` has a `from_logits` parameter to handle both logits and probabilities, but `FocalLoss` does not. This creates an inconsistency when using models with softmax activation and requires awkward workarounds. ```python model = smp.create_model(..., activation='softmax') outputs = model(x) # Probabilities [0, 1] # This works dice_loss = smp.losses.DiceLoss(mode='multiclass', from_logits=False) # This fails - FocalLoss applies softmax to output probabilities resulting in incorrect loss calc focal_loss = smp.losses.FocalLoss(mode='multiclass') ``` Currently need to either: 1. Remove model activation and use logits everywhere 2. Manually wrap `FocalLoss` to convert probabilities back to logits `FocalLoss` should have a `from_logits` parameter like `DiceLoss` for consistent API. _Environment_ - segmentation-models-pytorch version: 0.5.0 - PyTorch version: 2.7.1