Add numpy 2.0 support

Author: Roja Reddy Sareddy

tests/integ/sagemaker/feature_store/feature_processor/test_feature_processor_integ.py

@@ -1108,15 +1108,15 @@ def get_expected_dataframe():
     expected_dataframe = pd.read_csv(os.path.join(_FEATURE_PROCESSOR_DIR, "car-data.csv"))
     expected_dataframe["Model"].replace("^\d\d\d\d\s", "", regex=True, inplace=True)  # noqa: W605
     expected_dataframe["Mileage"].replace("(,)|(mi\.)", "", regex=True, inplace=True)  # noqa: W605
-    expected_dataframe["Mileage"].replace("Not available", np.NaN, inplace=True)
+    expected_dataframe["Mileage"].replace("Not available", np.nan, inplace=True)
     expected_dataframe["Price"].replace("\$", "", regex=True, inplace=True)  # noqa: W605
     expected_dataframe["Price"].replace(",", "", regex=True, inplace=True)
     expected_dataframe["MSRP"].replace(
         "(^MSRP\s\\$)|(,)", "", regex=True, inplace=True  # noqa: W605
     )
-    expected_dataframe["MSRP"].replace("Not specified", np.NaN, inplace=True)
+    expected_dataframe["MSRP"].replace("Not specified", np.nan, inplace=True)
     expected_dataframe["MSRP"].replace(
-        "\\$\d+[a-zA-Z\s]+", np.NaN, regex=True, inplace=True  # noqa: W605
+        "\\$\d+[a-zA-Z\s]+", np.nan, regex=True, inplace=True  # noqa: W605
     )
     expected_dataframe["Mileage"] = expected_dataframe["Mileage"].astype(float)
     expected_dataframe["Price"] = expected_dataframe["Price"].astype(float)

tests/integ/sagemaker/serve/test_tensorflow_serving_numpy2.py

@@ -61,7 +61,9 @@ def test_tensorflow_serving_with_sample_mlflow_model(self, sagemaker_session):
         logger.info("Testing TensorFlow Serving with sample MLflow model")
 
         # Use constant MLflow model structure from test data
-        mlflow_model_dir = os.path.join(DATA_DIR, "serve_resources", "mlflow", "tensorflow_numpy2_removed")
+        mlflow_model_dir = os.path.join(
+            DATA_DIR, "serve_resources", "mlflow", "tensorflow_numpy2_removed"
+        )
 
         # Create schema builder with numpy 2.0 arrays
         input_data = np.array([[1.0, 2.0, 3.0, 4.0]], dtype=np.float32)