diff --git a/models/tf.py b/models/tf.py
index 626d0f4bb..2fa1c25ac 100644
--- a/models/tf.py
+++ b/models/tf.py
@@ -1769,46 +1769,31 @@ def run(
     batch_size=1,  # batch size
     dynamic=False,  # dynamic batch size
 ):
-    # PyTorch model
-    """Def run(weights=ROOT / "yolov5s.pt", imgsz=(640, 640), batch_size=1, dynamic=False):"""
-        Exports YOLOv5 model from PyTorch to TensorFlow/Keras formats and performs inference for validation.
-    
-        Args:
-            weights (str | Path): Path to the pre-trained YOLOv5 weights file (typically a .pt file).
-            imgsz (tuple[int, int]): Tuple specifying the inference size (height, width) of the input images.
-            batch_size (int): Number of images to process in a batch.
-            dynamic (bool): Specifies dynamic batch size when set to True.
-    
-        Returns:
-            None: The function does not return any value. It displays model summaries and performs inference.
-    
-        Example:
-            ```python
-            run(weights="yolov5s.pt", imgsz=(640, 640), batch_size=1, dynamic=False)
-            ```
-    
-        Note:
-            - Ensure that the specified weight file path points to a valid YOLOv5 weight file, and the weights are compatible
-              with the model configuration.
-            - The function will load the PyTorch model, convert it to TensorFlow/Keras formats, and display the model
-              summaries. It will also perform a dummy inference to validate the export.
-        """
-        # PyTorch model
-        im = torch.zeros((batch_size, 3, *imgsz))  # BCHW image
-        model = attempt_load(weights, device=torch.device("cpu"), inplace=True, fuse=False)
-        _ = model(im)  # inference
-        model.info()
-    
-        # TensorFlow model
-        im = tf.zeros((batch_size, *imgsz, 3))  # BHWC image
-        tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
-        _ = tf_model.predict(im)  # inference
-    
-        # Keras model
-        im = keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size)
-        keras_model = keras.Model(inputs=im, outputs=tf_model.predict(im))
-        keras_model.summary()
     """
+    Exports YOLOv5 model from PyTorch to TensorFlow/Keras formats and performs inference for validation.
+
+    Args:
+        weights (str | Path): Path to the pre-trained YOLOv5 weights file (typically a .pt file).
+        imgsz (tuple[int, int]): Tuple specifying the inference size (height, width) of the input images.
+        batch_size (int): Number of images to process in a batch.
+        dynamic (bool): Specifies dynamic batch size when set to True.
+
+    Returns:
+        None: The function does not return any value. It displays model summaries and performs inference.
+
+    Example:
+        ```python
+        run(weights="yolov5s.pt", imgsz=(640, 640), batch_size=1, dynamic=False)
+        ```
+
+    Note:
+        - Ensure that the specified weight file path points to a valid YOLOv5 weight file, and the weights are compatible
+          with the model configuration.
+        - The function will load the PyTorch model, convert it to TensorFlow/Keras formats, and display the model
+          summaries. It will also perform a dummy inference to validate the export.
+    """
+
+    # PyTorch model
     im = torch.zeros((batch_size, 3, *imgsz))  # BCHW image
     model = attempt_load(weights, device=torch.device("cpu"), inplace=True, fuse=False)
     _ = model(im)  # inference