diff --git a/sagemaker-pipelines/tabular/local-mode/sagemaker-pipelines-local-mode.ipynb b/sagemaker-pipelines/tabular/local-mode/sagemaker-pipelines-local-mode.ipynb
index d836b75537..242a34de5e 100644
--- a/sagemaker-pipelines/tabular/local-mode/sagemaker-pipelines-local-mode.ipynb
+++ b/sagemaker-pipelines/tabular/local-mode/sagemaker-pipelines-local-mode.ipynb
@@ -375,9 +375,7 @@
     "    ],\n",
     "    outputs=[\n",
     "        ProcessingOutput(output_name=\"train\", source=\"/opt/ml/processing/train\"),\n",
-    "        ProcessingOutput(\n",
-    "            output_name=\"validation\", source=\"/opt/ml/processing/validation\"\n",
-    "        ),\n",
+    "        ProcessingOutput(output_name=\"validation\", source=\"/opt/ml/processing/validation\"),\n",
     "        ProcessingOutput(output_name=\"test\", source=\"/opt/ml/processing/test\"),\n",
     "    ],\n",
     "    code=\"code/preprocessing.py\",\n",
@@ -429,9 +427,7 @@
     "        header=None,\n",
     "    )\n",
     "    df_train = df_train.iloc[np.random.permutation(len(df_train))]\n",
-    "    df_train.columns = [\"target\"] + [\n",
-    "        f\"feature_{x}\" for x in range(df_train.shape[1] - 1)\n",
-    "    ]\n",
+    "    df_train.columns = [\"target\"] + [f\"feature_{x}\" for x in range(df_train.shape[1] - 1)]\n",
     "\n",
     "    try:\n",
     "        df_validation = pd.read_csv(\n",
@@ -479,18 +475,12 @@
     "    parser.add_argument(\"--tree_method\", type=str, default=\"auto\")\n",
     "    parser.add_argument(\"--predictor\", type=str, default=\"auto\")\n",
     "    parser.add_argument(\"--learning_rate\", type=str, default=\"auto\")\n",
-    "    parser.add_argument(\n",
-    "        \"--output_data_dir\", type=str, default=os.environ.get(\"SM_OUTPUT_DATA_DIR\")\n",
-    "    )\n",
+    "    parser.add_argument(\"--output_data_dir\", type=str, default=os.environ.get(\"SM_OUTPUT_DATA_DIR\"))\n",
     "    parser.add_argument(\"--model_dir\", type=str, default=os.environ.get(\"SM_MODEL_DIR\"))\n",
     "    parser.add_argument(\"--train\", type=str, default=os.environ.get(\"SM_CHANNEL_TRAIN\"))\n",
-    "    parser.add_argument(\n",
-    "        \"--validation\", type=str, default=os.environ.get(\"SM_CHANNEL_VALIDATION\")\n",
-    "    )\n",
+    "    parser.add_argument(\"--validation\", type=str, default=os.environ.get(\"SM_CHANNEL_VALIDATION\"))\n",
     "    parser.add_argument(\"--sm_hosts\", type=str, default=os.environ.get(\"SM_HOSTS\"))\n",
-    "    parser.add_argument(\n",
-    "        \"--sm_current_host\", type=str, default=os.environ.get(\"SM_CURRENT_HOST\")\n",
-    "    )\n",
+    "    parser.add_argument(\"--sm_current_host\", type=str, default=os.environ.get(\"SM_CURRENT_HOST\"))\n",
     "\n",
     "    args, _ = parser.parse_known_args()\n",
     "\n",
@@ -575,9 +565,7 @@
     "train_args = xgb_train.fit(\n",
     "    inputs={\n",
     "        \"train\": TrainingInput(\n",
-    "            s3_data=step_process.properties.ProcessingOutputConfig.Outputs[\n",
-    "                \"train\"\n",
-    "            ].S3Output.S3Uri,\n",
+    "            s3_data=step_process.properties.ProcessingOutputConfig.Outputs[\"train\"].S3Output.S3Uri,\n",
     "            content_type=\"text/csv\",\n",
     "        ),\n",
     "        \"validation\": TrainingInput(\n",
@@ -719,16 +707,12 @@
     "            destination=\"/opt/ml/processing/model\",\n",
     "        ),\n",
     "        ProcessingInput(\n",
-    "            source=step_process.properties.ProcessingOutputConfig.Outputs[\n",
-    "                \"test\"\n",
-    "            ].S3Output.S3Uri,\n",
+    "            source=step_process.properties.ProcessingOutputConfig.Outputs[\"test\"].S3Output.S3Uri,\n",
     "            destination=\"/opt/ml/processing/test\",\n",
     "        ),\n",
     "    ],\n",
     "    outputs=[\n",
-    "        ProcessingOutput(\n",
-    "            output_name=\"evaluation\", source=\"/opt/ml/processing/evaluation\"\n",
-    "        ),\n",
+    "        ProcessingOutput(output_name=\"evaluation\", source=\"/opt/ml/processing/evaluation\"),\n",
     "    ],\n",
     "    code=\"code/evaluation.py\",\n",
     ")"
@@ -815,14 +799,10 @@
     "        input_data = xgb.DMatrix(data=df)\n",
     "\n",
     "    else:\n",
-    "        raise ValueError(\n",
-    "            \"Content type {} is not supported.\".format(request_content_type)\n",
-    "        )\n",
+    "        raise ValueError(\"Content type {} is not supported.\".format(request_content_type))\n",
     "\n",
     "    prediction = model.predict(input_data)\n",
-    "    feature_contribs = model.predict(\n",
-    "        input_data, pred_contribs=True, validate_features=False\n",
-    "    )\n",
+    "    feature_contribs = model.predict(input_data, pred_contribs=True, validate_features=False)\n",
     "    output = np.hstack((prediction[:, np.newaxis], feature_contribs))\n",
     "\n",
     "    logging.info(\"Successfully completed transform job!\")\n",
@@ -1093,9 +1073,7 @@
    "source": [
     "# Get output files from processing job\n",
     "\n",
-    "processing_job_name = steps[\"PipelineExecutionSteps\"][0][\"Metadata\"][\"ProcessingJob\"][\n",
-    "    \"Arn\"\n",
-    "]\n",
+    "processing_job_name = steps[\"PipelineExecutionSteps\"][0][\"Metadata\"][\"ProcessingJob\"][\"Arn\"]\n",
     "outputs = local_pipeline_session.sagemaker_client.describe_processing_job(\n",
     "    ProcessingJobName=processing_job_name\n",
     ")[\"ProcessingOutputConfig\"][\"Outputs\"]\n",
@@ -1208,9 +1186,7 @@
     "    ],\n",
     "    outputs=[\n",
     "        ProcessingOutput(output_name=\"train\", source=\"/opt/ml/processing/train\"),\n",
-    "        ProcessingOutput(\n",
-    "            output_name=\"validation\", source=\"/opt/ml/processing/validation\"\n",
-    "        ),\n",
+    "        ProcessingOutput(output_name=\"validation\", source=\"/opt/ml/processing/validation\"),\n",
     "        ProcessingOutput(output_name=\"test\", source=\"/opt/ml/processing/test\"),\n",
     "    ],\n",
     "    code=\"code/preprocessing.py\",\n",
@@ -1261,9 +1237,7 @@
     "train_args = xgb_train.fit(\n",
     "    inputs={\n",
     "        \"train\": TrainingInput(\n",
-    "            s3_data=step_process.properties.ProcessingOutputConfig.Outputs[\n",
-    "                \"train\"\n",
-    "            ].S3Output.S3Uri,\n",
+    "            s3_data=step_process.properties.ProcessingOutputConfig.Outputs[\"train\"].S3Output.S3Uri,\n",
     "            content_type=\"text/csv\",\n",
     "        ),\n",
     "        \"validation\": TrainingInput(\n",
@@ -1306,16 +1280,12 @@
     "            destination=\"/opt/ml/processing/model\",\n",
     "        ),\n",
     "        ProcessingInput(\n",
-    "            source=step_process.properties.ProcessingOutputConfig.Outputs[\n",
-    "                \"test\"\n",
-    "            ].S3Output.S3Uri,\n",
+    "            source=step_process.properties.ProcessingOutputConfig.Outputs[\"test\"].S3Output.S3Uri,\n",
     "            destination=\"/opt/ml/processing/test\",\n",
     "        ),\n",
     "    ],\n",
     "    outputs=[\n",
-    "        ProcessingOutput(\n",
-    "            output_name=\"evaluation\", source=\"/opt/ml/processing/evaluation\"\n",
-    "        ),\n",
+    "        ProcessingOutput(output_name=\"evaluation\", source=\"/opt/ml/processing/evaluation\"),\n",
     "    ],\n",
     "    code=\"code/evaluation.py\",\n",
     ")\n",