breaking: fix unit tests and linting

NumpyDeserializer will not allow deserialization
unless allow_pickle flag is set to True explicitly

Author: Ashwin Krishna

src/sagemaker/base_deserializers.py

@@ -229,15 +229,15 @@ def deserialize(self, stream, content_type):
             if content_type == "application/x-npy":
                 try:
                     return np.load(io.BytesIO(stream.read()), allow_pickle=self.allow_pickle)
-                except ValueError as  ve:
-                    error_message = "Please set the param allow_pickle=True to deserialize pickle objects" + str(ve)
-                    raise ValueError(error_message)   
+                except ValueError as ve:
+                    raise ValueError("Please set the param allow_pickle=True \
+                        to deserialize pickle objects in NumpyDeserializer").with_traceback(ve.__traceback__)
             if content_type == "application/x-npz":
                 try:
                     return np.load(io.BytesIO(stream.read()), allow_pickle=self.allow_pickle)
-                except ValueError as  ve:
-                    error_message = "Please set the param allow_pickle=True to deserialize pickle objects" + str(ve)
-                    raise ValueError(error_message)   
+                except ValueError as ve:
+                    raise ValueError("Please set the param allow_pickle=True \
+                        to deserialize pickle objectsin NumpyDeserializer").with_traceback(ve.__traceback__)
                 finally:
                     stream.close()
         finally:

tests/unit/sagemaker/deserializers/test_deserializers.py

@@ -142,7 +142,8 @@ def test_numpy_deserializer_from_npy(numpy_deserializer):
     assert np.array_equal(array, result)
 
 
-def test_numpy_deserializer_from_npy_object_array(numpy_deserializer):
+def test_numpy_deserializer_from_npy_object_array():
+    numpy_deserializer = NumpyDeserializer(allow_pickle=True)
     array = np.array([{"a": "", "b": ""}, {"c": "", "d": ""}])
     stream = io.BytesIO()
     np.save(stream, array)