diff --git a/llmfoundry/eval/datasets/in_context_learning_evaluation.py b/llmfoundry/eval/datasets/in_context_learning_evaluation.py
index df5799df2b..8f317f60b8 100644
--- a/llmfoundry/eval/datasets/in_context_learning_evaluation.py
+++ b/llmfoundry/eval/datasets/in_context_learning_evaluation.py
@@ -478,7 +478,8 @@ def collate_fn(self, data: List[Dict[str, Any]]) -> Dict[str, Any]:
         batch['attention_mask'] = ~(batch['input_ids'] == self.pad_tok_id)
         return batch
 
-    def split_batch(self, batch: Any, microbatch_size: Union[int , float]) -> Sequence[Any]:
+    def split_batch(self, batch: Any,
+                    microbatch_size: Union[int, float]) -> Sequence[Any]:
         """Handling for certain specialty columns that must be split into.
 
         batches in different formats.
@@ -494,7 +495,8 @@ def split_batch(self, batch: Any, microbatch_size: Union[int , float]) -> Sequen
         # Normally split torch tensors
         # List split lists of strings
         if isinstance(microbatch_size, float):
-            raise ValueError('split_batch does not support floating point microbatch_size.')
+            raise ValueError(
+                'split_batch does not support floating point microbatch_size.')
         chunked = {}
         for k, v in batch.items():
             if k in self.static_keys:
@@ -907,7 +909,8 @@ def collate_fn(self, data: List[Dict[str, Any]]) -> Dict[str, Any]:
     def get_num_samples_in_batch(self, batch: Dict[str, torch.Tensor]) -> int:
         return batch['input_ids'].shape[0] // self.num_choices
 
-    def split_batch(self, batch: Any, microbatch_size: Union[int , float]) -> Sequence[Any]:
+    def split_batch(self, batch: Any,
+                    microbatch_size: Union[int, float]) -> Sequence[Any]:
         """Split batch while ensuring all continuations are in the same.
 
         microbatch.
@@ -925,7 +928,8 @@ def split_batch(self, batch: Any, microbatch_size: Union[int , float]) -> Sequen
             list: List of chunked batches
         """
         if isinstance(microbatch_size, float):
-            raise ValueError('split_batch does not support floating point microbatch_size.')
+            raise ValueError(
+                'split_batch does not support floating point microbatch_size.')
         chunked = {}
         for k, v in batch.items():
             if k in self.static_keys: