diff --git a/optimum/exporters/onnx/model_configs.py b/optimum/exporters/onnx/model_configs.py
index 392c34ca8fb..7c0dd3f8219 100644
--- a/optimum/exporters/onnx/model_configs.py
+++ b/optimum/exporters/onnx/model_configs.py
@@ -27,8 +27,8 @@
     BloomDummyPastKeyValuesGenerator,
     DummyAudioInputGenerator,
     DummyCodegenDecoderTextInputGenerator,
-    DummyDecoderTextInputGenerator,
     DummyDecisionTransformerInputGenerator,
+    DummyDecoderTextInputGenerator,
     DummyEncodecInputGenerator,
     DummyFluxTransformerTextInputGenerator,
     DummyFluxTransformerVisionInputGenerator,
@@ -265,19 +265,16 @@ class ImageGPTOnnxConfig(GPT2OnnxConfig):
 
 
 class DecisionTransformerOnnxConfig(GPT2OnnxConfig):
-    DUMMY_INPUT_GENERATOR_CLASSES = (
-        DummyDecisionTransformerInputGenerator,
-    )
+    DUMMY_INPUT_GENERATOR_CLASSES = (DummyDecisionTransformerInputGenerator,)
 
     @property
     def inputs(self) -> Dict[str, Dict[int, str]]:
-
         return {
-            'timesteps': {0: 'batch_size', 1: 'sequence_length'},
-            'returns_to_go': {0: 'batch_size', 1: 'sequence_length'},
-            'attention_mask': {0: 'batch_size', 1: 'sequence_length'},
-            'actions': {0: 'batch_size', 1: 'sequence_length', 2: 'act_dim'},
-            'states': {0: 'batch_size', 1: 'sequence_length', 2: 'state_dim'},
+            "timesteps": {0: "batch_size", 1: "sequence_length"},
+            "returns_to_go": {0: "batch_size", 1: "sequence_length"},
+            "attention_mask": {0: "batch_size", 1: "sequence_length"},
+            "actions": {0: "batch_size", 1: "sequence_length", 2: "act_dim"},
+            "states": {0: "batch_size", 1: "sequence_length", 2: "state_dim"},
         }
 
 
diff --git a/optimum/exporters/tasks.py b/optimum/exporters/tasks.py
index 08c5badebee..a4856f936ae 100644
--- a/optimum/exporters/tasks.py
+++ b/optimum/exporters/tasks.py
@@ -217,7 +217,9 @@ class TasksManager:
             "multiple-choice": "AutoModelForMultipleChoice",
             "object-detection": "AutoModelForObjectDetection",
             "question-answering": "AutoModelForQuestionAnswering",
-            "reinforcement-learning": ("AutoModel",), # multiple auto model families can be used for reinforcement-learning
+            "reinforcement-learning": (
+                "AutoModel",
+            ),  # multiple auto model families can be used for reinforcement-learning
             "semantic-segmentation": "AutoModelForSemanticSegmentation",
             "text-to-audio": ("AutoModelForTextToSpectrogram", "AutoModelForTextToWaveform"),
             "text-generation": "AutoModelForCausalLM",
diff --git a/optimum/utils/input_generators.py b/optimum/utils/input_generators.py
index b9fe8b22ed6..a6ce07bab32 100644
--- a/optimum/utils/input_generators.py
+++ b/optimum/utils/input_generators.py
@@ -513,11 +513,11 @@ class DummyDecisionTransformerInputGenerator(DummyTextInputGenerator):
     """
 
     SUPPORTED_INPUT_NAMES = (
-        'actions',
-        'timesteps',
-        'attention_mask',
-        'returns_to_go',
-        'states',
+        "actions",
+        "timesteps",
+        "attention_mask",
+        "returns_to_go",
+        "states",
     )
 
     def __init__(self, *args, **kwargs):
@@ -531,15 +531,15 @@ def generate(self, input_name: str, framework: str = "pt", int_dtype: str = "int
             shape = [self.batch_size, self.sequence_length, self.state_dim]
         elif input_name == "actions":
             shape = [self.batch_size, self.sequence_length, self.act_dim]
-        elif input_name == 'returns_to_go':
+        elif input_name == "returns_to_go":
             shape = [self.batch_size, self.sequence_length, 1]
         elif input_name == "attention_mask":
             shape = [self.batch_size, self.sequence_length]
-        elif input_name == 'timesteps':
+        elif input_name == "timesteps":
             shape = [self.batch_size, self.sequence_length]
             return self.random_int_tensor(shape=shape, max_value=self.max_ep_len, framework=framework, dtype=int_dtype)
 
-        return self.random_float_tensor(shape, min_value=-2., max_value=2., framework=framework, dtype=float_dtype)
+        return self.random_float_tensor(shape, min_value=-2.0, max_value=2.0, framework=framework, dtype=float_dtype)
 
 
 class DummySeq2SeqDecoderTextInputGenerator(DummyDecoderTextInputGenerator):