Add a tokenizer docstring for pali gemma

Otherwise the symbol would have mismatched code examples

Author: mattdangerw

keras_nlp/src/models/pali_gemma/pali_gemma_tokenizer.py

@@ -17,4 +17,63 @@
 
 @keras_nlp_export("keras_nlp.models.PaliGemmaTokenizer")
 class PaliGemmaTokenizer(GemmaTokenizer):
+    """PaliGemma tokenizer layer based on SentencePiece.
+
+    This tokenizer class will tokenize raw strings into integer sequences and
+    is based on `keras_nlp.tokenizers.SentencePieceTokenizer`. Unlike the
+    underlying tokenizer, it will check for all special tokens needed by
+    PaliGemma models and provides a `from_preset()` method to automatically
+    download a matching vocabulary for a PaliGemma preset.
+
+    If input is a batch of strings (rank > 0), the layer will output a
+    `tf.RaggedTensor` where the last dimension of the output is ragged.
+
+    If input is a scalar string (rank == 0), the layer will output a dense
+    `tf.Tensor` with static shape `[None]`.
+
+    Args:
+        proto: Either a `string` path to a SentencePiece proto file, or a
+            `bytes` object with a serialized SentencePiece proto. See the
+            [SentencePiece repository](https://github.com/google/sentencepiece)
+            for more details on the format.
+
+    Examples:
+
+    ```python
+    # Unbatched input.
+    tokenizer = keras_nlp.models.PaliGemmaTokenizer.from_preset(
+        "pali_gemma_3b_224"
+    )
+    tokenizer("The quick brown fox jumped.")
+
+    # Batched input.
+    tokenizer(["The quick brown fox jumped.", "The fox slept."])
+
+    # Detokenization.
+    tokenizer.detokenize(tokenizer("The quick brown fox jumped."))
+
+    # Custom vocabulary.
+    bytes_io = io.BytesIO()
+    ds = tf.data.Dataset.from_tensor_slices(["The quick brown fox jumped."])
+    sentencepiece.SentencePieceTrainer.train(
+        sentence_iterator=ds.as_numpy_iterator(),
+        model_writer=bytes_io,
+        vocab_size=8,
+        model_type="WORD",
+        pad_id=0,
+        bos_id=1,
+        eos_id=2,
+        unk_id=3,
+        pad_piece="<pad>",
+        bos_piece="<bos>",
+        eos_piece="<eos>",
+        unk_piece="<unk>",
+    )
+    tokenizer = keras_nlp.models.PaliGemmaTokenizer(
+        proto=bytes_io.getvalue(),
+    )
+    tokenizer("The quick brown fox jumped.")
+    ```
+    """
+
     pass