Lambdas to funcs

tests/huggingface/test_huggingface_classify.py

@@ -228,10 +228,11 @@ def test_cache_nested(model_and_tokenizer, atol):
         # for SentencePiece tokenizers like Llama's
         delim = ""
 
-    logits = lambda *args, **kwargs: hf._utils.logits_texts(*args, **kwargs)[0]
-    """
-    Returns next-token logits for each token in an inputted text.
-    """
+    def logits(*args, **kwargs) -> torch.Tensor:
+        """
+        Returns next-token logits for each token in an inputted text.
+        """
+        return hf._utils.logits_texts(*args, **kwargs)[0]
 
     with classify.cache(model_and_tokenizer, "a") as cached_a:
         with classify.cache(cached_a, delim + "b c") as cached_a_b_c:
@@ -241,9 +242,8 @@ def test_cache_nested(model_and_tokenizer, atol):
             logits3 = logits([delim + "1 2 3"], cached_a_b_c)
         logits4 = logits([delim + "b c d"], cached_a)
 
-    logits_correct = lambda texts, **kwargs: logits(
-        texts, model_and_tokenizer, drop_bos_token=False
-    )
+    def logits_correct(texts: Sequence[str], **kwargs) -> torch.Tensor:
+        return logits(texts, model_and_tokenizer, drop_bos_token=False)
 
     assert torch.allclose(logits1, logits_correct(["a b c d e f"]), atol=atol)
     assert torch.allclose(logits2, logits_correct(["a b c d x"]), atol=atol)