diff --git a/jupyter/rocm/tensorflow/ubi9-python-3.12/test/test_notebook.ipynb b/jupyter/rocm/tensorflow/ubi9-python-3.12/test/test_notebook.ipynb
index 65dd25cb1c..fe3596dc24 100644
--- a/jupyter/rocm/tensorflow/ubi9-python-3.12/test/test_notebook.ipynb
+++ b/jupyter/rocm/tensorflow/ubi9-python-3.12/test/test_notebook.ipynb
@@ -7,49 +7,53 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pathlib import Path\n",
     "import json\n",
     "import re\n",
     "import unittest\n",
+    "from pathlib import Path\n",
+    "from platform import python_version\n",
+    "\n",
     "import tensorflow as tf\n",
-    "import tensorboard\n",
     "import tf2onnx\n",
-    "from platform import python_version\n",
+    "\n",
     "\n",
     "def get_major_minor(s):\n",
-    "    return '.'.join(s.split('.')[:2])\n",
+    "    return \".\".join(s.split(\".\")[:2])\n",
+    "\n",
     "\n",
     "def load_expected_versions() -> dict:\n",
-    "    lock_file = Path('./expected_versions.json')\n",
+    "    lock_file = Path(\"./expected_versions.json\")\n",
     "    data = {}\n",
     "\n",
-    "    with open(lock_file, 'r') as file:\n",
+    "    with open(lock_file, \"r\") as file:\n",
     "        data = json.load(file)\n",
     "\n",
-    "    return data    \n",
+    "    return data\n",
+    "\n",
     "\n",
     "def get_expected_version(dependency_name: str) -> str:\n",
     "    raw_value = expected_versions.get(dependency_name)\n",
-    "    raw_version = re.sub(r'^\\D+', '', raw_value)\n",
-    "    return get_major_minor(raw_version) \n",
+    "    raw_version = re.sub(r\"^\\D+\", \"\", raw_value)\n",
+    "    return get_major_minor(raw_version)\n",
+    "\n",
     "\n",
     "class TestTensorflowNotebook(unittest.TestCase):\n",
-    "    \n",
+    "\n",
     "    def test_python_version(self):\n",
-    "        expected_major_minor = get_expected_version('Python')\n",
-    "        actual_major_minor = get_major_minor(python_version()) \n",
+    "        expected_major_minor = get_expected_version(\"Python\")\n",
+    "        actual_major_minor = get_major_minor(python_version())\n",
     "        self.assertEqual(actual_major_minor, expected_major_minor, \"incorrect version\")\n",
-    "    \n",
+    "\n",
     "    def test_tensorflow_version(self):\n",
-    "        expected_major_minor = get_expected_version('ROCm-TensorFlow')\n",
-    "        actual_major_minor = get_major_minor(tf.__version__) \n",
+    "        expected_major_minor = get_expected_version(\"ROCm-TensorFlow\")\n",
+    "        actual_major_minor = get_major_minor(tf.__version__)\n",
     "        self.assertEqual(actual_major_minor, expected_major_minor, \"incorrect version\")\n",
-    "        \n",
+    "\n",
     "    def test_tf2onnx_conversion(self):\n",
     "        # Replace this with an actual TensorFlow model conversion using tf2onnx\n",
     "        model = tf.keras.Sequential([tf.keras.layers.Dense(1, input_shape=(10,))])\n",
     "        onnx_model = tf2onnx.convert.from_keras(model)\n",
-    "        \n",
+    "\n",
     "        self.assertTrue(onnx_model is not None)\n",
     "\n",
     "    def test_mnist_model(self):\n",
@@ -59,18 +63,18 @@
     "        x_train, x_test = x_train / 255.0, x_test / 255.0\n",
     "        model = tf.keras.models.Sequential([\n",
     "          tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
-    "          tf.keras.layers.Dense(128, activation='relu'),\n",
+    "          tf.keras.layers.Dense(128, activation=\"relu\"),\n",
     "          tf.keras.layers.Dropout(0.2),\n",
     "          tf.keras.layers.Dense(10)\n",
     "        ])\n",
     "        predictions = model(x_train[:1]).numpy()\n",
-    "        predictions\n",
+    "        assert predictions\n",
     "        tf.nn.softmax(predictions).numpy()\n",
     "        loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
     "        loss_fn(y_train[:1], predictions).numpy()\n",
-    "        model.compile(optimizer='adam', loss=loss_fn, metrics=['accuracy'])\n",
+    "        model.compile(optimizer=\"adam\", loss=loss_fn, metrics=[\"accuracy\"])\n",
     "        model.fit(x_train, y_train, epochs=5)\n",
-    "        model.evaluate(x_test,  y_test, verbose=2)\n",
+    "        model.evaluate(x_test, y_test, verbose=2)\n",
     "        probability_model = tf.keras.Sequential([\n",
     "          model,\n",
     "          tf.keras.layers.Softmax()\n",
@@ -78,22 +82,26 @@
     "        probability_model(x_test[:5])\n",
     "\n",
     "    def test_tensorboard(self):\n",
+    "        # Check tensorboard is installed\n",
+    "        import tensorboard as _  # noqa: PLC0415, F401\n",
+    "\n",
     "        # Create a simple model\n",
     "        model = tf.keras.Sequential([\n",
-    "            tf.keras.layers.Dense(10, input_shape=(5,), activation='relu'),\n",
+    "            tf.keras.layers.Dense(10, input_shape=(5,), activation=\"relu\"),\n",
     "            tf.keras.layers.Dense(1)\n",
     "        ])\n",
     "        # Compile the model\n",
-    "        model.compile(optimizer='adam', loss='mse')\n",
+    "        model.compile(optimizer=\"adam\", loss=\"mse\")\n",
     "        # Generate some example data\n",
     "        x_train = tf.random.normal((100, 5))\n",
     "        y_train = tf.random.normal((100, 1))\n",
     "        # Create a TensorBoard callback\n",
-    "        log_dir = './logs'\n",
+    "        log_dir = \"./logs\"\n",
     "        tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir)\n",
     "        # Train the model\n",
     "        model.fit(x_train, y_train, epochs=2, callbacks=[tensorboard_callback])\n",
     "\n",
+    "\n",
     "expected_versions = load_expected_versions()\n",
     "\n",
     "suite = unittest.TestLoader().loadTestsFromTestCase(TestTensorflowNotebook)\n",