Pick the most accurate one-sentence summary of the tokenizer's role in fine-tuning.

**Option B is correct.** A tokenizer is a deterministic algorithm that splits raw text into subword pieces and assigns each piece a fixed integer ID. The IDs come from a vocabulary file built when the base model was pretrained. Common tokenizer algorithms include BPE, WordPiece, and SentencePiece Unigram. Those integer IDs are not random labels; they are row indices into the model's embedding matrix. If the tokenizer says the word 'cat' becomes ID 12345, the model looks up row 12345 of the embedding matrix to get the vector that represents 'cat'. The constraint that follows: the tokenizer used at fine-tuning and at inference must be exactly the same one the base model was pretrained with. If you swap it, the same text encodes to different IDs, those IDs index into the same embedding matrix, and the model retrieves the wrong vectors for every token. Outputs become garbled almost immediately. The other three options describe things that are not true. The tokenizer is not a neural network, it is not an optional runtime step, and it does not produce embeddings directly.

**Answer B.** A tokenizer is a fixed, pre-trained, non-neural algorithm that maps text to integer IDs. The IDs are row indices into the model's embedding matrix, so the integer 12,345 means 'fetch row 12,345 of the embedding table'. The tokenizer is shipped with the base model and is bound to it. The vocabulary file lists every subword token and its assigned ID; the algorithm (BPE, WordPiece, SentencePiece Unigram) is what produced those IDs from the original pretraining corpus. You can read it as a frozen lookup table. The consequence for fine-tuning is strict: use the same tokenizer end to end. The base model's embedding matrix has 32,000 (or 128,000, or whatever vocabulary size) specific rows arranged in a specific order. A different tokenizer would produce different IDs for the same text, and the embedding lookup would pull rows that mean something entirely unrelated. Quality collapses immediately. The distractors capture common misconceptions. Option A treats the tokenizer as trainable, which it is not, gradients do not flow into the tokenizer. Option C calls it optional, which it never is. Option D conflates the tokenizer with the embedding layer; the tokenizer produces integers, the embedding layer turns those integers into vectors.