release/0.19.0: bump version, update changelog, fix copyright headers

[TOPAPEC]

New lightweight module interface suited for training on largescale dataset with minimal overhead in terms of speed and memory consumption.

Added

• rectools.fast_transformers module — standalone transformer-based sequential recommenders that work directly with torch tensors, bypassing the Dataset/pandas pipeline. GPU-native sequence building via build_sequences() gives ~30x preprocessing speedup over SASRecDataPreparator on ML-20M (Adding UniSRec model implemented on lightweight class hierarchy with pytorch preprocessing  #306)
• FlatSASRecNet network — flat SASRec implementation without the ItemNet hierarchy. Pre-norm transformer encoder with id-embeddings, causal masking, softmax and BCE losses (Adding UniSRec model implemented on lightweight class hierarchy with pytorch preprocessing  #306)
• UniSRecNet network and UniSRecModel — sequential recommender with pretrained text embeddings (e.g. Qwen) and a learnable PCA/BN adaptor. Joint training of adaptor + transformer on pretrained embeddings. Configurable losses (softmax, BCE, gBCE, sampled_softmax), optimizers (Adam, AdamW), cosine warmup scheduler, early stopping, checkpoint save/load. UniSRecModel.fit() accepts raw (user_ids, item_ids, timestamps) tensors (Adding UniSRec model implemented on lightweight class hierarchy with pytorch preprocessing  #306)
• SequenceBatchDataset — lightweight torch Dataset wrapper for sequence training data (Adding UniSRec model implemented on lightweight class hierarchy with pytorch preprocessing  #306)