diff --git a/src/lerobot/scripts/eval.py b/src/lerobot/scripts/eval.py
index 872597340..4cbf89b27 100644
--- a/src/lerobot/scripts/eval.py
+++ b/src/lerobot/scripts/eval.py
@@ -46,6 +46,7 @@ Note that in both examples, the repo/folder should contain at least `config.json
 You can learn about the CLI options for this script in the `EvalPipelineConfig` in lerobot/configs/eval.py
 """
 
+import concurrent
 import json
 import logging
 import threading
@@ -56,7 +57,7 @@ from copy import deepcopy
 from dataclasses import asdict
 from pathlib import Path
 from pprint import pformat
-import concurrent
+
 import einops
 import gymnasium as gym
 import numpy as np
@@ -158,7 +159,7 @@ def rollout(
         observation = add_envs_task(env, observation)
         with torch.inference_mode():
             action = policy.select_action(observation)
-        observation['observation.images.image']
+        observation["observation.images.image"]
         # Convert to CPU / numpy.
         action = action.to("cpu").numpy()
         assert action.ndim == 2, "Action dimensions should be (batch, action_dim)"
@@ -177,12 +178,11 @@ def rollout(
 
         # Keep track of which environments are done so far.
         # done = terminated | truncated | done
-        #TODO: jadechoghari changed, this is cleaner
+        # TODO: jadechoghari changed, this is cleaner
         done = terminated | truncated | done
         if step + 1 == max_steps:
             done = np.ones_like(done, dtype=bool)
 
-
         all_actions.append(torch.from_numpy(action))
         all_rewards.append(torch.from_numpy(reward))
         all_dones.append(torch.from_numpy(done))
@@ -378,7 +378,7 @@ def eval_policy(
     # Wait till all video rendering threads are done.
     for thread in threads:
         thread.join()
-    
+
     # Compile eval info.
     info = {
         "per_episode": [
@@ -460,16 +460,22 @@ def _compile_episode_data(
 
     return data_dict
 
+
 def set_global_seed(seed):
     """Set seed for reproducibility."""
     import random
+
     random.seed(seed)
     np.random.seed(seed)
     torch.manual_seed(seed)
     if torch.cuda.is_available():
         torch.cuda.manual_seed_all(seed)
+
+
 def log_output_dir(out_dir):
-    logging.info("Output dir:"+ f" {out_dir}")
+    logging.info("Output dir:" + f" {out_dir}")
+
+
 @parser.wrap()
 def eval(cfg: EvalPipelineConfig):
     logging.info(pformat(asdict(cfg)))